Search

Uncertainty surrounds farming systems across Europe and strengthening their resilience lies at the centre of the European policy agenda. Although farming systems´ resilience has been widely conceptualised, no consensus has been reached about assessing the contribution of farm and farmer attributes to farmers´ perceived resilience by quantitative approaches. The aim of this study was to understand what farm(er) attributes and principles contribute to explain farmers´ perceived resilience. Our specific objectives are to: i) develop a conceptual framework composed of attributes, principles and capacities to assess farms' resilience, including farmer personal resilience as a resilience principle; ii) quantify links between farm attributes and resilience principles with farmers´ perceived resilience capacities. We developed a framework that includes different farm and farmer attributes grouped into resilience principles. We designed and conducted a structured survey to allow small ruminant farmers in Spain to self-assess their resilience attributes and capacities. We used structural equation modelling to assess to what extent resilience attributes and principles explain perceived robustness, adaptability, transformability capacities and overall resilience. Farmers´ perceived resilience can be explained by several resilience attributes and principles, including farmers' personal resilience. Some attributes contribute similarly to robustness, adaptability and transformability, while others contribute particularly to each capacity. Farm diversity, tightness of feedbacks and farmers´ personal resilience were key for explaining farmers´ perceived resilience for small ruminant farming systems in Spain. In particular, farmer optimism, and farms' ability to respond in different ways to challenges and to overcome difficulties in the past, were the attributes that most influenced resilience perceptions. Our results highlight the importance of farmer personality, in addition to farm characteristics, for understanding farmers' resilience perceptions. This study contributes to the development of quantitative farm resilience assessments by considering multiple farm attributes and also several farmers' psychological attributes. Our framework provides a list of attributes and principles that can be applied to different farming systems. We provide a specific approach to identify the most relevant attributes and principles that drive perceived resilience in a large set of them that could guide farm and stakeholders' decision making. [Display omitted] • Farm(er) attributes explain farmers´ perceived robustness, adaptability and transformability. • Farmers´ perceived resilience can be quantified with attributes and principles by Structural Equation Modelling. • Personal resilience, diversity and tightness of feedbacks explain farmers´ perceived resilience. • Optimistic farmers with diverse alternatives to face challenges perceive farms as more resilient. [ABSTRACT FROM AUTHOR]

Small woody features (SWF), as field boundaries, hedgerows, or riparian buffers, are crucial for agricultural landscapes and, frequently, disregarded. In combination with agricultural land uses they are considered agroforestry systems (AF SWF), but their spatial distribution and detailed location of SWF types are insufficiently known in the EU as to support agricultural policies or enhance the development of farming practices for biodiversity conservation or productivity management. In addressing this, the LUCAS 2015 dataset was analysed across EU member states to identify, characterise, and determine the extent and distribution of AF SWF classes and the variety of SWF types in agricultural lands. Additionally, a comparison between AF SWF and common agroforestry systems (AF C), such as silvopastoral, silvoarable, grazed or intercropped permanent crops, and kitchen gardens was conducted. To achieve this, four categories of AF SWF were established based on the classes of land cover within agricultural areas where SWF are present: arable crops AF SWF , grazed grasslands AF SWF , ungrazed grasslands AF SWF , and permanent crops AF SWF. The typology and relevance of the AF SWF categories and the SWF types were analysed and mapped at country level and by biogeographical regions. The spatial distribution of AF SWF and the different types of SWF were analysed using density maps. Results reveal that AF SWF cover 443,770 km2 (10% of the EU-28 and 25% of the utilised agricultural area). This area encompasses arable crops (44%), ungrazed grasslands (24%), grazed grasslands (23%), and permanent crops (8%). The extent of AF SWF is 3.3 times larger than AF C (132,317 km2), being mainly concentrated in Ireland, United Kingdom, France, Denmark, and Germany, while AF C prevail in the Mediterranean. As regards to SWF types, both managed and unmanaged hedgerows were dominant in France, Great Britain, and Ireland. Heaths and shrubs in Spain and Germany. Grove and woodlands margins in Spain, while avenue trees were dominant in Germany. Single trees and conifer hedges, the less prevalent SWF types, were broadly distributed. This pioneering research addresses a knowledge gap, thoroughly documenting AF SWF , revealing both its types and spatial distribution. The findings highlight substantial disparities in AF SWF prevalence among member states of the EU. The study compares AF SWF with AF C in relevance and distribution, significantly contributing to better understanding agroforestry systems and offering baselines for future monitoring and management. Findings advocate for policy incentives and increased awareness among farmers to foster the understanding of the impacts of SWF on productivity and biodiversity. [Display omitted] • LUCAS data provide valuable insights into the typology and spatial distribution of SWF in agricultural areas. • SWF agroforestry systems cover 3.3 times more land area than common agroforestry systems in the European Union. • Arable crops associated with SWF were the most extensive agroforestry systems. • Hedgerows are the most frequent type of SWF within agricultural areas. • SWF have the potential to enhance ecosystem services in common agroforestry systems. [ABSTRACT FROM AUTHOR]

Society has a number of expectations around livestock farming that go beyond mere production and affordable food prices to now encompass high standards of animal welfare and environmental performance. Here we investigate whether and how it is possible to concurrently achieve good economic, environmental, and animal welfare performances on suckler cattle farms. We extracted economic indicators, proxies for animal welfare and environmental performances, and data describing farming practices and conditions from a technical-economic database featuring data collected from >250 French suckler farms over the period 2016–2022. We analysed the relationships between animal welfare performance, environmental performance and economic performance using a structural equation modelling (SEM) approach. We then used logit models to identify farming practices and conditions that promote 'multiperformance'. Farms that combine practices where nutritional needs of suckler cattle are synchronised with the grass availability cycle are more likely to multiperform. The synchronisation is managed by exploiting certain key animal characteristics (depletion and restoral of body reserves), choosing the right calving season, and selling animals well adapted to grass-feeding. Combining two analytical models—one establishing the relationships between several performance dimensions and one establishing the relationships between multiperformance and farming practices—allows to bridge the gap between theoretical concepts and concrete farming measures on the topical issue of achieving multiperformance in more than two dimensions, where the literature is still scarce. [Display omitted] • Society wants livestock farms to concurrently deliver animal welfare, environmental and economic performances. • We investigate whether and how multiperformance is achievable based on technical-economic data of 250 suckler cattle farms. • We use structural equation modelling to assess the concepts of animal welfare, environmental and economic performances. • There is a positive moderately-strong link between the three performance dimensions. • Combining practices that synchronise cattle nutritional needs with the grass growth cycle is a multiperformance strategy. [ABSTRACT FROM AUTHOR]

Farm general resilience, i.e. capacity to deal with the unknown, uncertainty and surprise, is increasingly considered a must in today's uncertain and changing context. To measure this resilience, assessments against predefined resilience properties (e.g. diversity, connectivity) are gaining currency. We posit that these assessments neglect a key dimension of farming activity: working conditions of farmers and other farm workers. Our objective was to verify whether farm general resilience always matches good working conditions. We adopted a mixed approach combining structured and semi-structured interviews with participant observation on four contrasting organic dairy family farms located in Aveyron, France. We applied a resilience assessment based on predefined properties and an assessment of working conditions (e.g. availability of suitable infrastructures and equipment, workforce, work organization) and compared their outcomes. Farm D and B had the highest mean resilience scores (2.4 and 2.1 respectively, on a 1–3 scale). They outperformed other farms on several properties, especially diversity and connectivity, which was maximized by interactions between productions to recycle nutrients. Farm A had the least resilient profile (1.4) with, among other things, limited autonomy. Farm C shared some of these weaknesses, but it outperformed other farms regarding profitability (3.0). Assessment of working conditions provided very different patterns. Farm C had the best working conditions (2.7) and a well-balanced profile across those conditions. Farm A displayed overall good working conditions (2.4). Farm D (2.1) was impaired by highly centralized decision-making processes and limited free time. Farm B had the worst working conditions (1.5) due to a lack of planning and an excessive workload that wore out workers and degraded their relations. There was no clear relation between farm general resilience and working conditions. Still, the cases of farms B and D illustrate the criticality of working conditions on farm general resilience. Poor working conditions may lead farmers to cessation or burn out and affect these farms' ability to remain over the long term. This work raises the issue of working conditions in farm general resilience assessments. It clearly shows the need to improve assessment frameworks used by agricultural scientists to further take into account working conditions of farmers and other farm workers as a cornerstone of farm general resilience. Another challenge lies in the identification of farm configurations enabling the joint achievement of both good working conditions and farm general resilience. [Display omitted] • We posit that farm resilience assessments based on pre-defined properties neglect working conditions. • We compare the outputs of a resilience assessment and an assessment of working conditions on four organic dairy family farms. • We show that apparently resilient farms can offer poor and sometimes with critically low working conditions. • Poor working conditions compromise farm capacity to remain over the long term by leading farmers to cessation or burn out. • Agricultural scientists have to consider working conditions as a cornerstone of farm resilience. [ABSTRACT FROM AUTHOR]

Pesticide use in agriculture has serious impacts on the environment, biodiversity and human health. Even though these strong negative impacts have been identified for decades, the reduction of phytosanitary products is becoming increasingly urgent. Agricultural land is a socio-ecological system in which environmental, economic, agronomic and social components are closely linked and interact in a non-linear and complex way. As such, it has become evident that pesticide reduction can only be achieved by jointly considering these different elements of the socio-ecosystem. In this article, we first discuss the behavioural factors involved in changing agricultural practices with a focus on pesticide practices. We then attempt to assess the respective influence of these factors on farming practices. Finally, we analyse how these behavioural factors could be used to induce concrete changes towards the adoption of environmentally friendly practices and question their consideration in future research. To do so, we undertake a literature review: we analyse a wide range of articles using a behavioural science framework. To anchor our work in agricultural reality, we illustrate the review of the behavioural factors using verbatim transcriptions taken from several interviews with farmers. Based on our corpus, we focus on nine articles to better understand the relative influences of these factors in the studies and highlight five case studies to explore the activation of these factors through action levers. We identify fourteen factors operating at three different levels: individual, social and external. These factors likely interact with each other, thus enhancing their effect on the changes in agricultural practices. Some behavioural factors described in this review are not explored in the few articles that attempt to compare their importance, thus rendering our understanding of their relative importance only partial. We observe that some factors are more easily translated into levers for changing pesticide practices, although this depends on the scale of the studies and the object of change under consideration. We believe that the behavioural sciences can provide a better insight into the multiple dynamics at play with regard to the changes in agricultural practices and production systems. We hope that this article will not only strengthen the relevance and use of the behavioural sciences to address these issues but will also allow for a more realistic conceptualisation of farming behaviours in future research. [Display omitted] • The agricultural land is a socio-ecosystem where many actors and components coexist and influence the agricultural practices on the territory. • We identify the behavioural factors linked with these components that are involved in changing farming practices. • We highlight and categorise three types of factors, at several levels: individual, social and external, and attempt to rank them. • Some factors are even more relevant for changing farming and pesticides practices, and more easily translated into interventions. • In-depth work on the synergies between these factors and consistent measures are needed in future research. [ABSTRACT FROM AUTHOR]

Use of Nitrogen (N), an essential macronutrient, must be optimised in order to ensure food security and food sovereignty, mitigate negative externalities of food production and achieve ambitious (inter-)national environmental, climate and sustainability goals. Nitrogen use efficiency (NUE) is an appropriate indicator for assessing N utilisation on farms. The aim of the study was to evaluate N performance of the German agricultural sector, to generate knowledge gain regarding methodological design and estimating N indicators based on farm accounting data, to estimate the role and extent of externalities, and to increase understanding of interrelations between farm characteristics and N performance in order to support policymakers in finding targeted N mitigation measures. Using data from the German Farm Accountancy Data Network (FADN) covering 5923 farms between the years 2016/17 and 2018/19, we calculated mean farm-level NUE and N balance values for six farm types, considering input and output parameters on farm level as well as externalities. We also developed two explanatory models to identify interrelations between N performance indicators investigated, and regional, socio-economic and farm structural characteristics. The results indicated an increasing trend in NUE from dairy, to pig and poultry, and arable farms, but large variance within each farm type, indicating efficiency reserves in N use. Livestock farms undercut NUE and exceed N surpluses to such an extent that the sustainability goal for 2030 for the national N balance as defined in German Sustainable Development Strategy could be jeopardised. Different levels of externalisation, namely feed imports and manure exports, may deepen this gap. Multiple regression analysis revealed statistically significant interrelations between N performance and independent variables such as soil fertility, crop selection and diversity, production type, operating profit and consulting services received. Thereby, structural patterns and strategies in order to reduce N waste and increase NUE were identified. However, main source of uncertainty was due to the lack on imported manure amounts from FADN data. Determination of N performance can improve understanding the complexity of agri-environmental interrelations and support policymakers in designing appropriate policies to improve N management. [Display omitted] • We calculated Nitrogen (N) use efficiencies and N balances for different farm types using German Farm Accountancy Data Network. • We found high variance in N performance within farm types for 2017 to 2019, indicating efficiency reserves in N utilisation. • Based on calculated N indicators, farms with animals would miss national Sustainable Development Goals for 2030. • Nitrogen indicators were interrelated with regional, farm structural and socio-economic factors. [ABSTRACT FROM AUTHOR]

Farm typologies help to identify patterns across a wide range of farm systems and describe heterogeneity in agriculture concisely. They can also support the design of agricultural policies by providing information and knowledge about policy target groups. For example, voluntary agri-environmental schemes could be tailored to specific agricultural landscapes and farm types. Farm typologies, however, are often developed from scratch, with limited connection to previous studies and policy making. The objective of this study is to clarify the purposes of farm typologies in research and agricultural policy making and to develop a framework that allows to increase the usefulness and usability of farm typologies for agricultural policy making. Based on a review of 13 systematically identified overview studies on farm typologies, we develop a framework that establishes connections between the purposes of farm typologies along the different stages of the policy process. We find multiple purposes for farm typologies, the two most common of which are for understanding the characteristics, heterogeneity, and development of farm systems and for policy making. The newly developed framework suggests that connecting knowledge across these purposes could improve the validity, transferability, and relevance of farm typologies for agricultural policy making. Our framework also provides an entry point for encouraging cooperation between developers and users of typologies, and for the improvement of typologies through new data (including behavioural data) and methods such as machine learning. We conclude that future research can build on the existing work on farm typologies but must be aware of the specific challenges that are associated with the use of farm typologies in the policy process. Knowledge of the prospects and challenges of using farm typologies allows to increase the usefulness and usabilityof these typologies and can contribute to the design of targeted and tailored agricultural policy instruments. By increasing the acceptance, perceived fairness, and legitimacy, this can improve their effectiveness and efficiency, which is urgently needed for a successful transformation to a more sustainable agricultural sector. [Display omitted] • Tailoring policies to target groups can improve their effectiveness and efficiency. • We provide a "review of reviews" of the purposes of farm typologies. • A framework links purposes of farm typologies to the stages of the policy process. • This linkage improves the validity, transferability, and relevance of farm typologies. • Cooperation between developers and users increases the usability of farm typologies. [ABSTRACT FROM AUTHOR]

In Ethiopia, similar to other parts of Sub-Saharan Africa, the effects of farmland degradation on ecosystem services and well-being are especially prominent as 85% of the population lives in rural areas and depends on agriculture for their livelihoods. Existing farmer-led restoration activities involve integrating trees on farms (ToF) through traditional agroforestry or woodlots systems. Although ToF practices can potentially bring economic and ecological gains, their production systems and efficiency in terms of input use remain overlooked in the current literature. The study analyses the technical efficiency levels in Ethiopia's home gardens and woodlot systems and examines the drivers contributing to technical efficiency. The study estimates the technical efficiency of farmers who implement the ToF strategies, i.e. the extent to which the farmers can achieve the highest output level given their set of inputs. Deviation from the total technical efficiency is then referred to as unrealised potential. We also analyse the drivers of technical inefficiency by controlling for geographical, biophysical, and production system attributes of woodlots and agroforestry patches, as well as household characteristics and institutional settings. We use a one-step Latent Class Stochastic Frontier Modelling approach based on the cross-sectional household data of 543 home gardens and 426 woodlot systems patches in Ethiopia. According to the results, in woodlots, extensive farms lose 38% and intensive farms 22% of the potential output. In home-gardens, intensive farms lose 39%, and extensive farms lose 49% of the output due to technical inefficiency. Significant determinants of technical efficiency in both systems are ecological zones, access to markets, communal carbon payments, forest user groups, training, land title factors, rotation age and product diversification, among others. Our results help identify priority policy and practical action fields for improving farmer-led restoration strategies' technical efficiency and management in Ethiopia and the global south. Future national reforestation policies could reduce inefficiencies in farmer-led systems across Sub-Saharan Africa by targeting the factors exacerbating technical inefficiency per class in a given strategy. Thus, handling these drivers increases the success of farmer-led restoration strategies and contributes to achieving restoration pledges and AFR100 commitments in mosaic landscapes. [Display omitted] • We examined technical efficiency (TE) of farmer-led restoration strategies, Woodlots and Home-gardens. • To control heterogeneity, we used a Latent Class Stochastic Frontier Model to estimate TE and its drivers. • We obtain mainly two classes in both systems, labour-intensive and labour-extensive farms. • Unrealised potential ranges between 22 and 49% and differs among classes within a restoration system. • Geographical, household, institutional and product system drivers contribute to model estimation and affect class TE. [ABSTRACT FROM AUTHOR]

Agricultural land suitability analysis (ALSA) for crop production is one of the key tools for ensuring sustainable agriculture and for attaining the current global food security goal in line with the Sustainability Development Goals (SDGs) of United Nations. Although some review studies addressed land suitability, few of them specifically focused on land suitability analysis for agriculture. Furthermore, previous reviews have not reflected on the impact of climate change on future land suitability and how this can be addressed or integrated into ALSA methods. In the context of global environmental changes and sustainable agriculture debate, we showed from the current review that ALSA is a worldwide land use planning approach. We reported from the reviewed articles 69 frequently used factors in ALSA. These factors were further categorized in climatic conditions (16), nutrients and favorable soils (34 of soil and landscape), water availability in the root zone (8 for hydrology and irrigation) and socio-economic and technical requirements (11). Also, in getting a complete view of crop's ecosystems and factors that can explain and improve yield, inherent local socio-economic factors should be considered. We showed that this aspect has been often omitted in most of the ALSA modeling with only 38% of the total reviewed article using socio-economic factors. Also, only 30% of the studies included uncertainty and sensitivity analysis in their modeling process. We found limited inclusions of climate change in the application of the ALSA. We emphasize that incorporating current and future climate change projections in ALSA is the way forward for sustainable or optimum agriculture and food security. To this end, qualitative and quantitative approaches must be integrated into a unique ALSA system (Hybrid Land Evaluation System - HLES) to improve the land evaluation approach. • Methods used in agricultural land suitability analysis (ALSA) strengths and limitations • Uncertainty and sensitivity analysis inclusion in ALSA modeling process • Climate change analysis inclusion in ALSA task • Frequently used predictors in ALSA modeling including biophysical, socioeconomic and management practices [ABSTRACT FROM AUTHOR]

The economic and environmental sustainability of farming systems is under increased pressure from extreme climate variability and market volatility. The diversification of large-scale farms can increase profits, resilience, and deliver sustainability outputs. To (a) propose a farm diversification framework specifically designed for large-scale commercial farms; and (b) demonstrate its application to assess the benefits and trade-offs associated with alternative diversification options for a case-study farm in Northern Queensland, Australia. A literature review and discussions with large-scale commercial farmers, a Natural Resource Management NGO, and a regional council were used to co-design our framework. The framework was then used to guide discussions with a case-study farmer and conduct a participatory modelling exercise on alternative cropping-based diversification scenarios. We used a simulation model to produce quantitative information on the present and co-designed alternative scenarios, to iteratively inform discussions with the participating case-study farmer. The simulated scenarios included (i) the current system of dryland (non-irrigated) monocropping of cotton, (ii) a dryland cotton-maize rotation, (iii) an irrigated cotton-maize rotation, and (iv) an irrigated maize-mungbean-cotton rotation. The benefits and trade-offs of each scenario were assessed across five performance measures: downside risk, profit, water-use efficiency, ground cover and, from the discussion with the participating farmer, the skills needed to de-risk the diversification process. Results from the co-designed scenarios showed that adding dryland maize as a rotation crop can reduce downside risks by 12.5% and increase fractional ground cover by more than two-fold, i.e., from 20% to >40%, compared to the current system. The use of irrigation in the cropping system increased the median gross margin by $2000 ha−1 and reduced downside risks to zero. The water-use efficiencies of the proposed irrigated scenarios were similar. Farmers reported that developing skills in irrigated cropping could increase their capacity to dynamically chose more profitable crops to sow with certainty of higher returns, but it would require up-front investments in terms of money and time. Our proposed framework can be used to assess benefits and trade-offs of alternative farm diversification options across a range of performance dimensions (risk; profit; resource-use efficiency; skills). These assessments will ensure that the co-designed options are evaluated across multiple dimensions, all within the local context of the study and farmers' preference(s) and circumstances. The use of a co-design approach can be expected to increase learnings for all those involved and increase the chances of practice change and adoption. [Display omitted] • A farm diversification analysis framework can inform the discussion between science, practice & governance. • Important analysis dimensions include risks, profits, resource-use efficiency & skills. • The co-designed diversification options fitted farmers' preferences and were more likely to be adopted. [ABSTRACT FROM AUTHOR]

Farmers are forced to undertake adaptive measures to protect their crops against climate change impacts. Fruit trees present farmers with unique adaptive challenges. In pursuit to expand the knowledge base on climate change effects on crops in underreported regions, we present herein data from Chile and Tunisia on the vulnerability of cherry and peach orchards to climate change and on possible measures to counter its impact. We aimed to ascertain a) the extent to which fruit farming biophysical operations in Chile and Tunisia are vulnerable to climate change impacts, b) whether the vulnerability can be reduced through agronomic, technological or financial adaptive measures and, c) how to overcome challenges that may prevent implementation of these measures. The study is based on face-to-face interviews with 801 peach and cherry farmers in Tunisia and Chile and three focus meetings with farm representatives. Fruit farmers have used and are planning to use a number of agricultural, technological, and financial tools to reduce the vulnerability of their crops to climate change. Agronomical measures are focused on planting varieties with higher drought and disease resistance as well as lower winter chill requirements. Technological measures focus on installing nets reflects farmer regional experiences with crop hail damage and rain at the fruit maturing stage. Planned improvements in irrigation reflect anticipated future problems with water availability and drought. The decision not to implement adaptive measures is mainly linked to economic barriers. Specific actions are proposed to overcome these and other measure implementation barriers. The self-reported experiences of fruit farmers provide valuable insight into the effectiveness of various climate change adaptive measures, which often differ from scientific expectations or recommendations. For example, farmers are not overly concerned about fulfilling winter chill requirements, they are also not well informed about all available climate change adaptive tools. In addition, many adaptive measures are not implemented due to administrative or financial barriers. To help address this, regionally-led cooperation between farmers, policy makers, industry, and scientists is needed to identify and overcome these barriers, allowing for the successful implementation of appropriate adaptive measures. [Display omitted] • Background: Vulnerability assessment provides the means to identify specific actions needed to minimize climate change impacts on farms • Objectives: Ascertain the extent to which cherry and peach farms in Chile and Tunisia are vulnerable to climate change impacts • Results: Farmers predict drought and water availability issues will affect their farming operations over the next ten years the most • Conclusions/significance: Address climate change impacts at a regional level, focusing on barriers hindering implementation of adaptive measures [ABSTRACT FROM AUTHOR]

Crop-type composition and configuration at field and landscape scales are known to impact ecological functions in agricultural landscapes. However, links between regional farmland structures (e.g., variable farm size, ownership, configuration, etc.), which are important determinants of farm-level management decisions, and subsequent changes in agroecosystem impacts, remain understudied. Objective: To investigate these dynamics, we examined relationships between changing farm structures, as indicated by farm consolidation and persistence, and concurrent changes in crop diversity and agricultural resource use between 2002 and 2018 in Kern County, California. Methods: We integrated parcel-level datasets documenting farm size, operatorship, crop cover, pesticide usage and evapotranspiration rates for 2002 and 2018. We then applied seemingly unrelated regression analyses to quantify potential relationships between these key variables. Results and conclusions: In our study area we observed low (28%) persistence of farm operators from 2002 to 2018 and farm consolidation, as evidenced by a 43% increase in farm midpoint size in the studied time period. We further found significant associations between farm structures and agro-environmental outcomes. A doubling in farm size was associated with an 8% increase in crop diversity, but also with an 18% increase in pesticide-use and a 7% increase in water use at the farm level, raising concerns about the impact of large farms on landscape-scale resource use. Both crop diversity and water use were higher in persistent farms (8% and 11%, respectively) than in replaced or emergent farms. Our findings illustrate how regional shifts in farm size and operatorship may link to changes in crop diversity and resource use. Given continued concerns about the impacts that landscape simplification, pesticide use, and water shortages may have on agroecosystems both in our study region and beyond, our study highlights regional farm structure dynamics as a key element to investigate further and to consider in policies aiming to increase the sustainability of future agricultural landscapes. [Display omitted] • We study structural shifts in our study area by analyzing changes in farm size and operatorship from 2002 to 2018. • We then assess potential links to changing farm-level crop diversity, pesticide and water use through regression analysis. • Our data indicates high operator turnover and farm consolidation across Kern County, driven by emerging farms. • Persistent farms had higher crop diversity and water use; large farms had higher crop diversity and pesticide & water use. • Our findings highlight regional farm structure dynamics as a key element to investigate further. [ABSTRACT FROM AUTHOR]

Climbing beans offer potential for sustainable intensification in the East-African highlands, but their introduction requires a major change in the cropping system compared with the commonly grown bush bean. We explored farm-level opportunities, constraints and trade-offs for climbing bean cultivation in the eastern highlands of Uganda. We established current food self-sufficiency, income, investment costs and labour, and assessed the ex-ante , farm-level impact of four climbing bean options on these indicators. Input for this assessment were a detailed characterization of 16 farms of four types, and on-farm, experimental data of adaptation trials of climbing bean. Climbing beans generally improved food self-sufficiency and income, but often required increased financial investment and always demanded more labour than current farm configurations. Opportunities for integration of climbing beans on small farms were limited. Although some of the poorest farmers accrued the largest absolute benefits from climbing beans, their ability to make the necessary investments is questionable. The analysis was translated into a simple-to-use modelling tool to enable participatory analysis of the outcomes with farmers of the four farm types to understand their perspectives and decision-making. The discussions revealed a recent increase in market prices for climbing bean resulting in growing interest in their cultivation in the eastern highlands. A lack of seed and stakes was limiting climbing bean cultivation, and a sufficient amount of climbing bean seed needs to be ensured through strengthening of farmer cooperatives and improved storage. [ABSTRACT FROM AUTHOR]

In a context of a severe funding crisis, donors and policymakers expect increased accountability from research organizations and convincing proof that public investments in research have significant and positive societal impacts. This article takes stock of the lessons learned from the use of a method (ImpresS) designed by CIRAD to analyze the impact of research undertaken in partnership with a range of different actors in a developing-country context. The method uses a case study approach, and relies on the evaluation of the impact pathway and on contribution analysis. Thirteen case studies were selected to represent the diversity of partnerships, research activities and types of innovation. The results confirm the diversity and complexity of the innovation processes encompassing the non-linearity of changes over extended periods, the diversity of impacts, the shifting roles of actors engaged in the innovation process, and the diversity of activities carried out by the research community to contribute to outcome and impact generation. Interactions between researchers and other actors throughout the innovation process appeared to play key roles along the impact pathway. Based on the 13 case studies, we identified four generic models through which research contributes to impact: participatory transfer of knowledge and technologies, co-design of innovation, support for the innovation process, and promotion of open innovation. Our results underline the need for research institutions to recognize and accept the diversity of functions fulfilled by researchers if they want to contribute in an effective manner to the generation of impacts. Another challenge is to learn how to take advantage of clusters of projects embedded in innovation pathways in order to sustain research activities over a long timeframe. Significance statement Impact evaluation is increasingly being requested from the research community as a measure of accountability by both donors and civil society. Conducting it properly is challenging, especially in the context of developing countries. Quantitative studies are often biased toward expected and tangible impacts. Complementary qualitative approaches are focused on understanding causality and are more in line with the actors' participation in impact evaluation. CIRAD has developed a method and used it to assess 13 case studies involving research conducted in partnership in widely differing environments. Some main lessons learned include the long timeframe needed for impacts to be achieved, the diversity of impacts the research community needs to consider, and the multiple roles played by the research community in co-developing outcomes with diverse stakeholders. Results show that the research community can contribute to impacts by using several models of intervention. [ABSTRACT FROM AUTHOR]

Given that agriculture is a key economic activity of the majority of people living in rural Africa, agricultural development is at the top of the agenda of African leaders. Intensification of agriculture is considered an entry point to improve food security and income generation in sub-Saharan African (SSA). We used a farm optimization model to perform ex-ante assessment of scenarios that could improve gross margin, a farmer's objective, and maize sales, a national policy objective to improve food security, of large and small farms in maize-based farming systems in two posts representative of rural Mozambique (Dombe and Zembe Administrative Posts in Central Province). For selling maize, farmers first had to be maize self-sufficient. We explored two options for increasing agricultural productivity: (i) extensification, to expand the current cultivated area; and (ii) intensification, to increase input use per unit of land. We considered two scenarios for each of the two options. Extensification: current situation (SC1), hired labour (SC2) and labour-saving (SC3). Intensification: land-saving (SC4) and combined improvement (SC5). For each scenario, we maximized gross margin and maize sales for large and small farms and assessed the trade-offs between the two goals. We further explored the impact of increasing labour and land availability at farm level beyond the current observed levels. SC4 substantially increased both gross margin and maize sales of large and small farms in both posts. Minor trade-offs were observed between the two goals on large farms whereas we saw synergies between the goals for small farms. In Dombe, the gross margin of large farms increased from $ 5550 to $ 7530 y -1 and maize sales from 12.4 t to 30.4 t y -1 . In Zembe, the annual gross margin increased from $ 1130 up to $ 2410 per farm and annual maize sales from 5.1 t up to 9.5 t per farm. For small farms in Dombe, the gross margin increased from $ 1820 to $ 2390 y -1 and maize sales from 3.0 t to 9 t y -1 . In Zembe, the annual gross margin increased from $ 260 to $ 810 and annual maize sales from 2.0 t to 3.6 t per farm. With the most optimistic scenarios and conditions of more hired labour and labour-saving technologies, both farm types substantially increased both gross margin and maize sales. We conclude that with available resources, the possibilities for increasing gross margin and maize sales are greater where agroecological conditions are more favourable and are much higher for larger farms. Without interventions that allow small farms to access more labour and land, intensification of agriculture is likely to happen only on farms of better-resourced households, indicating the need for alternative forms of on- and off-farm income generation for poorer farmers. The contribution of agriculture to national food security has to come from the large farms, requiring policy support. [ABSTRACT FROM AUTHOR]

Integrated Agricultural Research for Development (IAR4D) is a mode of participatory action research for development that aims to improve the well-being of smallholder farming households by facilitating networks between farmers and marketplace actors through groups called ‘innovation platforms’. In 2012, the Association for the Promotion of Livestock in the Sahel and Savannas (APESS, an NGO with rural membership) established nine innovation platforms in the Sahelian zone in Senegal, Burkina Faso, Cameroon and Chad, with the aim of engaging member livestock producers with meat and milk processors and traders. In late 2013 and early 2014, each innovation platform reviewed its perception of “performance” or “functionality”, using a consensus-based group assessment scored against nine criteria. The aim of this study is to test the extent to which perceived functionality was related to the activities of the innovation platform, as well as current farm productivity and profitability, household well-being, and regional context, using data gathered independently by APESS as part of its broader mandate to improve the lives of members. Across the nine innovation platforms, there were strong positive correlations between the responses to all self-assessment criteria i.e. members perceived that their innovation platform was performing strongly or weakly in all respects. The criterion scores were correlated positively with the number of meetings held, but not with numbers or gender of attendees, training opportunities, nor actions agreed at meetings. Innovation platforms self-assessed more positively in southern, higher rainfall regions than in the northern, lower rainfall regions. Performance was positively correlated with many measures of farm productivity and household well-being, including median area of farms, value of crop and hay production, sales of animals or animal products, expenditure on agricultural inputs, current engagement with markets, and estimated number of months per year that the family is able to feed itself. We conclude that the IAR4D approach is perceived to work well in high rainfall zones, for smallholders with a relatively high current level of well-being, who have larger and more productive farms, and who are already engaged with markets. The approach does not appear to work well in more arid regions for poorer smallholders, who are often not able to provide food for their families for 12 months of every year. The IAR4D focus on social networking into markets appears to be relevant for producers who wish to strengthen or diversify existing market relationships, but is not perceived to be sufficiently relevant by smallholders for whom achieving reliable subsistence and averting starvation remains the priority. [ABSTRACT FROM AUTHOR]

Low nitrogen (NUE) and phosphorus (PUE) use efficiencies in food production in China result in large losses of nitrogen (N) and phosphorus (P) to the environment. The Chinese government formulated policies to increase the NUEs and PUEs. Recent policies aim for zero growth in synthetic fertilizer use after 2020 while ensuring food security. In this study we analyzed how current and improved nutrient management in China can affect future nutrient use efficiencies and nutrient losses from food production. The NUEs and PUEs of food production were quantified using the NUFER (NUtrient flows in Food chains, Environment and Resources use) model for 31 provinces and China in 2013, 2020 and 2050. Results show that national NUE (20%) and PUE (24%) in 2013 are low but vary largely among provinces (12–33% for NUE, 10–53% for PUE). The N and P losses to the air (14 Tg year − 1 of N) and waters (12 Tg year − 1 of N, 2 Tg year − 1 of P) are consequently high in 2013. Three scenarios were analyzed for 2020 and 2050 to explore future trends in NUEs and PUEs, assuming Business As Usual ( BAU ) trends, Zero Fertilizer ( ZF ) growth from 2020 , and Improved Nutrient Management ( INM ). In the BAU scenario, the NUEs and PUEs roughly remain at their low 2013 levels, while nutrient inputs to agriculture are increasing. The losses to the air therefore increase by 37% for N and to waters by 40% for N and 48% for P between 2013 and 2050. In the ZF scenario, the NUEs and PUEs are a few percent higher than in BAU . The associated N and P losses to waters are 8–16% lower than in BAU because of increased recycling of manure to cropland, but still higher than in 2013. Improved nutrient management, as assumed in our INM scenario, may increase NUEs and PUEs to 33% and 59% in 2050. Meanwhile, N and P losses to waters in 2050 are 47% and 83% lower than in 2013, and losses of N to the air 20% lower. We conclude that the policy aimed at zero growth in fertilizer use is a good start, but not very effective in reducing nutrient pollution in China. To substantially reduce N and P losses to the environment it is needed to improve nutrient management by not only reducing fertilization without yield losses, but also by improved manure management and animal production with lower nutrient excretion. [ABSTRACT FROM AUTHOR]

Arable farming systems in the Netherlands are characterized by crop rotations in which potato, sugar beet, spring onion, winter wheat and spring barley are the most important crops. The objectives of this study were to decompose crop yield gaps within such rotations into efficiency, resource and technology yield gaps and to explain those yield gaps based on observed cropping frequencies and alternative farmers' objectives. Data from specialized Dutch arable farms between 2008 and 2012 were used. Production frontiers and efficiency yield gaps were estimated using the stochastic frontier framework. The resource yield gap was quantified through the estimation of highest farmers' yields (Y HF , average across farms with actual yields above the 90th percentile). Crop model simulations and variety trials were compiled to assess climatic potential yields (Yp) and technology yield gaps. The contribution of crop area shares and farmers' objectives to actual yields were assessed using regression analysis and based on five different farm level indicators (N production, energy production, gross margin, nitrogen-use efficiency and labour use), respectively. The average yield gap per crop (as percentage of Yp which is given in parentheses) was: 29.2% (of 72.6 t ha −1 ) for ware potato, 39.7% (of 71.6 t ha −1 ) for starch potato, 26.4% (of 107.1 t ha −1 ) for sugar beet, 32.3% (of 88.3 t ha −1 ) for spring onion, 25.2% (of 12.3 t ha −1 ) for winter wheat and 37.5% (of 10.4 t ha −1 ) for spring barley. The efficiency yield gap ranged between 6.6% (starch potato) and 18.1% (spring onion) of Yp. The resource yield gap was lower than 10% of Yp for all the crops and the technology yield gap ranged between 7.1% (ware potato) and 30.7% of Yp (starch potato). There were statistically significant effects of potato (positive quadratic) and onion (positive) area shares on ware potato, sugar beet and winter wheat yields, of sugar beet area share (positive quadratic) on winter wheat yield and of cereal area share (negative) on sugar beet and winter wheat yields. Farmers' objectives explain part of the variability observed in crop yields which were 7–24%, 13–24% and 12–32% lower than Y HF , respectively, for gross margin maximising, labour minimising and N use efficiency maximising farms. In addition, there was a significant positive relationship between gross margin and the yield of ware potato, sugar beet and winter wheat. By contrast, no significant relationships were found between crop yields and NUE or labour use. We conclude that most of the yield gap is explained by the efficiency yield gap for ware potato and spring onion and by both the efficiency and technology yield gaps for sugar beet and cereals. The resource yield gap explains most of the yield gap of seed potato, and the technology yield gap of starch potato. The results regarding the effects of cropping frequency and crop rotations to crop yields are not very conclusive which suggest that agronomic principles become less evident at ‘systems level’ given the number of interacting factors at crop rotation level. Finally, although N and energy production are lower for gross margin maximising farms, most crop yields are not significantly different between farms with the highest N and energy production compared to farms performing best on economic (gross margin) objectives. [ABSTRACT FROM AUTHOR]

Most stockless organic farms depend on the import of organic nitrogen. Biogas digestates offer an interesting solution to address this need for flexible nitrogen fertilizers. Their application could support the conversion of specialized arable farms, contributing to the politically targeted expansion of organic production. However, various regulations on the use of off-farm biogas digestates exist, which differ considerably in allowed N imports. Despite the growing interest in the application of biogas digestates in stockless organic farming in practice and research, its impacts on the economic potential of converting from conventional to organic farming have not been investigated. This study assesses the economic potential of organic production for specialized arable farms without taking up animal production based on cooperating with a conventional biogas plant. The study considers the impacts of different regulations on importing off-farm biogas digestates. The assessment employs the bio-economic farm model FarmDyn to evaluate multiple economic performance indicators for three stockless arable case study farms with varying cropping patterns under conventional and organic production. The German federal state of North Rhine-Westphalia serves as the case study area. A large-scale sensitivity analysis quantifies the impact of relevant parameters with a high uncertainty or possible large impact. Our results suggest that organic farming has a high economic potential for specialized arable farms when biogas digestate is applied. Taking existing subsidies into account, organic farming economically outperforms conventional production for all assessed farms and regulatory scenarios. However, stronger restrictions on the application of biogas digestates shift crop rotations toward higher shares of crops with low nutrient requirements and legumes. This reduces, especially in case of fodder legumes, revenues and increases labor requirements, and lowers profitability and labor productivity. Distance to the biogas plant and subsidies for organic production impact strongly on profitability, whereas input prices show small effects. Results underline that the economic performance of stockless organic farming depends highly on import possibilities of nutrients. Furthermore, they suggest that subsidies for organic farming should better reflect its economic potential across farm types to reduce deadweight effects and boost conversion where it is costly. The study is the first to assess the impact of different regulations governing the import of fermentation substrates on the economic potential of stockless organic farming for specialized arable farms. This is relevant as conversion of stockless arable farms is lagging behind but could considerably contribute to reach policy targets for organic production. [Display omitted] • We assess the economic potential of organic production for specialized arable farms based on the import of biogas ferment. • Stockless organic farming has high economic potential, outperforming conventional farming for all assessed case study farms. • Differences in regulations on import of off-farm nitrogen in fermentation substrate impact strongly the economic potential. • The economic potential of organic farming depend strongly on the organic subsidy and the distance to the biogas plant. • Conversion of stockless arable farming is economically attractive and supports existing policy goals for organic production. [ABSTRACT FROM AUTHOR]

In Ethiopia, many agricultural interventions have been introduced for mixed farm types in different biophysical and socioeconomic conditions. The contribution of such interventions to multi-objectives at farm level and beyond remains unclear. To derive insights into interaction of multi-objectives on economic profit-, nutrition security-, and environmental performance that are relevant for improvement of farm household living income in Amhara, Ethiopia. FarmDESIGN model evaluated performance of four stylized medium- and small-scale farm types in a low (Lay Gayint)- and high (South Achefer)- potential district: LG-M, LG-S, SA-M and SA-S. Pareto-based multi-objective optimization was performed to maximize farm profit, livestock density, dietary energy and vitamin A yield, and minimize GHG and soil N losses. Further analysis resulted in 3–4 clusters per farm type with varied farm configurations. Higher dietary energy- and vitamin A- yield that meets household and Ethiopian society requirements increased synergistically with profit in three of the four farm type clusters, while LG-S clusters could only increase vitamin A yield substantially with profit. Only LG-M clusters intensified livestock by addition of 4–8 crossbred cows and 1–10 chickens, supported by imported feed and off-farm grazing but face strong environmental trade-offs of increased GHG and soil N losses. Chicken production (eggs and chicken meat) is an option to reduce environmental impact, increase profit and contribute to both human nutrition requirements. In LG-S, SA-M and SA-S farm type clusters, profit was generated mainly from sale of eucalyptus, maize, potato, carrot, finger millet, garlic, beetroot and banana, with eucalyptus accounting at least 35% of the crop profit. Livestock diversification in these clusters also contributed to farm revenue, but always to a smaller extent, due to relatively high management costs. A living income from farming activities was only achieved on SA-M3 as 3.96 USD/capita/day (+0.36 above the threshold). This cluster had a 2.90 ha land area, farm income almost equally generated from crop and livestock production, and successfully met all set objectives except maximizing livestock density. Our study infers the need to increase land area, expand poultry and rear crossbred cows, and cultivate high value crops to realize a living income solely from agricultural activities. For implementation, current land-use policies should support farm area expansion. The suggested agricultural options, which already align with current Ethiopian development plans, must utilize sustainable measures that will not lead to short- or long-term challenges as higher GHG and soil nutrient mining. [Display omitted] • Four contrasting stylized farm types from a low and high potential district in Amhara, Ethiopia were modelled. • Stakeholders were allowed to prioritize objectives resulting in 15 clusters of the generated farm configurations. • The results give insight into agricultural options that meet multi-objectives in different pillars of sustainability. • Poultry was a promising short-term option to meet all set objectives, and cultivation of eucalyptus was highly profitable. • To meet a living income solely from agricultural activities in this region, farm size should be a minimum of 2.9 ha. [ABSTRACT FROM AUTHOR]

Nitrogen (N) is an essential input for food production, but at the same time N raises serious concerns for air, water and soil pollution due to its overuse. Research shows that N use is influenced by socio-economic factors beyond the environmental circumstances while heavily depending on the agronomic practices and technologies used. Micro-level studies based on real farm observations are still scarce in this context. Therefore, this study estimated N use efficiency (NUE) and its climatic and socio-economic determinants, as well as N 2 O and NH 3 emissions from organic and inorganic fertiliser-amended fields. Through exploring primary data collected from 155 wheat growers from north-western Bangladesh, we estimated NUE of wheat production as the ratio of the total quantity of N produced in the harvested grain and the total quantity of N applied to soil through inorganic and organic fertilisers. Then we estimate a Tobit model to determine the socio-economic and farm-level practices influencing NUE. Finally, for N 2 O and NH 3 emissions, we applied region-specific emission factors, which are not sensitive to management practices but can give an approximation of emission and emission intensity of the production. The estimated mean NUE for North-West Bangladesh was 37.4%, which widely varied across different farm categories and farming practices used. Agronomic practices, including those induced by climate change, positively contributed to NUE. NUE was higher for the farmers cultivating high-yielding, drought-tolerant, and short-duration wheat varieties. NUE significantly increased when a farmer applied a combination of organic and inorganic fertilisers. Irrigation water pricing was critical in NUE since it influences farmers' water management practices. A notable quantity of N loss as N 2 O (5.98 kg/ha) and NH 3 (45.58 kg/ha) was emitted, and the farms managed by landless and marginal farmers emitted quantities more than others. Farmers with better landholding size, education and access to extension services operated at a relatively higher level of NUE. The policy suggestions to increase NUE include strengthening extension services, particularly for promoting a balanced supply of nutrients from organic and inorganic fertilisers, promoting volumetric irrigation water pricing and encouraging long-term tenurial arrangement. The paper is relevant to policy makers since, unlike earlier works based on experimental data, it estimates more robust indices of NUE and considers the role of different farms and farmer-specific socio-economic, anthropogenic and institutional factors in farm production. Finally, the paper estimates N 2 O and NH 3 emissions associated with wheat production. [Display omitted] • Low nitrogen use efficiency (NUE) is a concern in crop production in Bangladesh, along with many countries globally. • Analysing farm survey data, the paper estimated NUE, gaseous emissions inferred based on presumed defaults. • The estimated mean NUE was 37.4% which was found to be related to several socioeconomic factors. • Farm data, as opposed to experimental data, allowed assessing the effects of variables on NUE that would be hard otherwise. • The paper is novel in finding agronomic, socioeconomic and environmental determinants of NUE including climate change. [ABSTRACT FROM AUTHOR]

The potential of marginal lands to improve food security, support bioenergy production or ecosystem services has globally got a lot of attention. Defining agricultural marginal land is a task that involves more than just considering the land's quality, its definition changed a lot during the last two centuries. Development of new technologies and policy trends require the concepts of prime land and marginal land to be renewed from time to time. Although much research has been done on the concept of marginal land, it is currently limited by the lack of a clear, globally accepted definition. There are four major sources of criteria of marginal lands: economic (e.g., rent cost, land value), geographical (e.g., temperature, slope, precipitation), ecosystem-based (e.g., protected areas, recreation, ecosystem services), soil suitability (e.g., yield capability, physical and chemical soil properties). The categorisation of agricultural land into groups like productive, marginal or unproductive often depends on the cultivation or management type. Since conceptions of marginal land are dynamic both in time and space, flexible policy and practical solutions are needed for their non-degrading use, which in any case shall support nature-based socioeconomic development. To maintain the socioeconomic value of these areas, it is crucial to develop rural areas that are economically or biophysically marginalised. High nature value farming, bioenergy crops by sustainable land management and afforestation are highly recommended. Choosing the right management can transform marginal land into an optimal soil condition or incorrect management can degrade prime land into marginal land (unproductive land). This paper provides a review and categorisation of the historical and new developments of marginal land concepts especially those which are working with agricultural aspects, including land management and reclamation. It could give a strong basis for further research in topic of marginal land. [Display omitted] • Marginal lands are depending on the spatial scale and the regional conditions. • Marginal land can be turned suitable for the objective of the use. • Marginal lands are dynamic both in time and space. • Flexible policy and practical solutions are needed. [ABSTRACT FROM AUTHOR]

Accelerated global warming in the cold-climate boreal and Arctic regions facilitates intensification and expansion of agriculture. However, current boreal and Arctic governmental policies targeting agricultural development differ among regions creating the risk for serious consequences for provision of goods and ecosystem services. On June 29th, 2021 we organized a United Nations Summit dialogue on "Local and global food security shaped by northern agriculture" to complement a 2019 survey of the scientific community on boreal and Arctic agriculture. The 33 attendees concluded that while climate change might offer significant agricultural opportunities in boreal and Arctic communities such as increased food security, sovereignty, and economic development, this should not be considered lightly and without strong support for environmental sustainability as carbon and biodiversity losses are main risks. Attendees expressed that using existing agricultural lands more efficiently is likely a better option than converting forest to new agricultural lands. It was also confirmed by the attendees that consistent policies are needed across northern jurisdictions that, above all else, consider environmental consequences followed by local food needs and interests. We and the attendees propose that this can only be ensured through further research and effective dialogues which reflect the needs and concerns of all members of the northern communities, including farmers. [Display omitted] • Potential for short-term benefits to be realized at large-scale are questionable. • Agricultural practices evolved in other climatic regions must be adapted to local conditions. • Agricultural development strategies must consider fundamental changes to the forestry sector. • Agricultural Policies are fragmented which limits coherent development and capacity building. • Inefficient communication and networking limit development and impairs human health. [ABSTRACT FROM AUTHOR]

This research evaluates the economic and environmental impacts of land allocation to crops and extensive livestock production in the Argentine Pampas using compromise programming. Two economic indicators (gross margin, direct costs) and five environmental indicators (organic carbon input to soil, nutrient balances, agrochemicals impact and soil erosion) are considered. The tradeoff between economic and environmental objectives is assessed and the preferred land allocation schemes determined by the multicriteria model are compared to the current land use in the region of Pergamino, North of Buenos Aires, Argentina. Results indicate that it is important to consider not only the environmental-economic tradeoff in farming, but also the conflict across different environmental criteria in order to determine whether it is reasonable to stimulate activities which improve some environmental indicators at the expense of getting less desirable values in others. For instance, an increase of productivity in agriculture enhances soil organic carbon content but it may increase the risk of contamination with agrochemicals and nutrients. Results are consistent with the notion that extensive crop-livestock production systems are more balanced than continuous crop farming. According to model results, optimal land assignment is a combination of crop and livestock activities (62 and 38% of land, respectively) which is associated with lower agrochemical use, greater organic carbon input to soil, better soil protection from erosion and more efficient nutrient cycling. This land assignment presents a decrease of 20% in the economic gross margin compared to the continuous agriculture scheme that maximizes the economic result. The comparison between current land use and the optimal land assignment based on model results show that farmers assign a smaller fraction of land to extensive livestock production (2–4%), than in the most balanced compromise solution (38%). Results also suggest that it would be appropriate to encourage farmers to reduce the area of land assigned to full-season soybeans. [ABSTRACT FROM AUTHOR]

To understand the effects of policy changes on organisations and compositions of cropping systems at regional scale and their contribution to the sustainable development of regions, we built a regional, spatially explicit, multi-scale, bioeconomic model called MOSAICA. This model explicitly incorporates information at field, farm, sub-regional and regional scales to provide cropping system mosaics by way of regional optimisation of the sum of individual farmer's utilities under field, farm and territory biophysical and socio-economic constraints. Its generic structure means it can be used in different regions with geographic information on the location of the field and farm, data on cropping system performance, on location factors and on policy schemes. We used the model in Guadeloupe to test the impact of three scenarios of change on the agricultural subsidy regimes. The model produced three cropping system mosaics which reduced the area under banana and sugarcane, turned specialised banana and sugarcane farming systems into breeding systems while improving the overall contribution of agriculture to sustainable development. The spatially explicit results of changes in ecosystem services, and in farming systems with MOSAICA make it an appropriate decision-aid tool for regional planning. [ABSTRACT FROM AUTHOR]

Farms contribute heavily to overall sustainability. To date, various frameworks, approaches, methods and indicators have been developed to appraise how much corporations (firms, farms) contribute to sustainability. Among these, the Sustainable Value (SV) method helps to determine by which economic entity resources should be used in order to achieve the highest contributions to sustainability, by comparing company's efficiency of capital use against the efficiency of a benchmark. In this work we argue that the SV benchmark does not include certain crucial environmental concepts, such as the carrying capacity of an ecosystem or the multiple vital functions provided by ecosystem services. Thus, it is not a suitable reference for companies to evaluate their contribution to sustainability. As an alternative benchmark to standard SV applications, we propose a farm whose resource use productivity is correlated to the fulfilment of pre-determined environmental constraints: the Environmentally Sustainable Farm (ESF). To design this farm, we created an agro-environmental farm modelling framework that includes: (i) methods and approaches to assess indicators concerning specific environmental issues; (ii) an integrated ecological–economic model based on linear programming. This farm modelling framework is employed to determine both the environmental performance and the economic returns of two farming systems, conventional (CFS) and organic (OFS), which are compared using the Sustainable Value approach. Despite the fact that the OFS environmentally outperforms the CFS, none of them reaches the sustainable performances achieved by the ESF, both performing a negative Sustainable Value. The environmental constraints forced the ESF towards management choices that, although decreasing the economic performance, increase its environmental sustainability behaviour. Hence, the ESF is a viable benchmark that can be used to evaluate farms' environmental sustainability. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a decision model focused on farm planning under risk. [•] Model uses operation research to understand the resource allocations under risk. [•] Increasing the intensification of land’s use will increase farm’s returns and risks. [•] Double crop production system has a limit to increase farm’s returns. [Copyright &y& Elsevier]

Highlights: [•] We use survey data to analyze agricultural land system change in Northeast China. [•] Land system changes analyzed include land tenure, crop choice and intensification. [•] Spatial patterns of these aspects are related to road accessibility. [•] Crops for domestic use are increasingly replaced by cash crops in the study region. [•] Family characteristics affect land transfer while external drivers affect crop choice. [ABSTRACT FROM AUTHOR]

Abstract: A ca. 2010, 30m resolution map depicting annually tilled areas across the conterminous United States was developed. Input sources included fouryears, spanning 2008–2011, of annual national-level coverage Cropland Data Layer (CDL) land cover classifications as produced by the National Agricultural Statistics Service. Derived total land area under tillage from the aggregate CDL product equaled 112.8million hectares (278.7million acres). By comparison, the 2007 Census of Agriculture (CoA) produced an estimate of 122.9million hectares, suggesting the map is under representing tilled area by 10.1million hectares or 8.2%. Regression analysis using state-level summaries showed a strong, albeit biased, correlation (r-squared=0.99) between the CDL derived tilled area and the CoA information. Notable outliers were North Dakota and Montana. Comparisons of the CDL tilled map were also made against the 2006 National Land Cover Dataset (NLCD) land cover product’s Cultivated Crops category. Strong state-level regression agreement (r-squared=0.98) was also found between the NLCD and the CDL acreages, but the NLCD estimated 8.5% more area than the CDL and thus closely matched that of the CoA. However, significant pixel level differences were found between the CDL and the NLCD. Nationally 5.6% of the maps were in disagreement as to whether cultivated or not, a large proportion considering around a seventh of the country’s land area is tilled. States of Arkansas, Montana and Wisconsin had the largest absolute discrepancies between the NLCD and CDL. Accepting the CDL as reference showed a national level NLCD cropland commission error of 23.0% and omission error of 14.5%. Much of what is believed to be problematic in the NLCD could be explained by definitional issues having included alfalfa hay into their cultivated category for many areas. Ultimately, while it is likely that the CDL annually tilled area model is an underestimate of the true total, taken contextually in map form and adjusted for undercount bias it likely is the best available. [Copyright &y& Elsevier]

Abstract: Fertilizers that are spread on agricultural fields can leach into aquifers and contaminate groundwater sources for drinking water particularly with nitrate. Modeling this phenomenon can help in evaluating the impact of current or future agricultural practices on nitrate content within an aquifer. The three-dimensional Water flow and Nitrate transport Global Model (WNGM), that was previously developed and applied to a well-capture zone, is actually used to simulate future land management scenarios over the same zone. The studied area is located in a region with intensive agricultural practice in Southern Québec (Canada). The sandy unconfined aquifer underlying the agricultural fields is highly vulnerable to nitrate contamination from ground surface and nitrate concentrations around critical threshold of 45mg/L were observed in the municipal well. Using the calibrated WNGM, a number of land management scenarios were simulated in order to predict future trends of nitrate concentrations in the pumping well from 2008 through 2040 that could be induced by changes in land management practices over this well-capture zone. More specifically, these scenarios simulate implementation of improved or ideal agricultural practices within the agricultural portion of the well-capture zone, and modification of the pumping rate at the municipal well. The results show how a decrease and stabilization of nitrate concentration should occur at the municipal well after a period varying from 5 to 30years depending on the implemented agricultural modifications. It is also shown that, in this particular case, an increase of the pumping rate at the municipal well from 700 to 2800m3/d would lead to an increase of the well-capture zone from 0.8 to 2.5km2 and to a decrease of nitrate concentrations at the municipal well from 43 to 34mg/L. Furthermore, it is predicted that application of improved agricultural practices in the vicinity of this municipal well could lead to a reduction of nitrate concentrations at the well of about 8mg/L. [Copyright &y& Elsevier]

Abstract: Changes in the structure of agriculture are known to affect emissions of environmental pollutants from agriculture. Such changes are often driven by structural changes in agricultural production, so structural changes are likely to have indirect effects on emissions. In a pilot study, we consider how linking two complementary simulation models might be used to explore these effects. The agent-based AgriPoliS model was used to simulate the structural dynamics of agricultural production. The results from AgriPoliS were passed via a number of intermediate models to the Farm-N model, which was used to estimate the nitrogen surplus and losses from each farm for each year. The modelling complex was exercised by simulating the effects of two plausible policy scenarios for each of 14years. The initial sizes and types of farms were based on statistics from a region in Denmark and the farms were randomly distributed within this region. The reference scenario (REF) implemented the current area-based Common Agricultural Policy payments for Denmark. The 1LU scenario applied the additional constraint that a minimum area of 1ha land had to be available for the application of the manure produced by one livestock unit. Substantial changes in the structure of agricultural production were shown for both scenarios. The effect on the regional nitrogen surpluses was predicted to differ between scenarios and the contribution of the different farm types to change with time. Predicted ammonia emission changed with time and differed between the scenarios, whereas the Danish fertiliser and manure legislation meant that nitrate leaching remained fairly stable. The implementation of additional environmental legislation significantly changed the trajectory of structural adjustment processes. Results emphasize the complex interplay between structural changes, losses of nitrogen, and environmental regulation. It is concluded that the effects of structural change on environmental emissions can be usefully explored by linking agent-based models of farmers’ investment decisions with other models describing nutrient losses from the farm and that such modelling can play a useful role in designing effective environmental policies for agriculture. However, the approach demands the availability or collection of many region-specific data and this could create a barrier to its use. [Copyright &y& Elsevier]

Abstract: Technological interventions to address the problem of poor productivity of smallholder agricultural systems must be designed to target socially diverse and spatially heterogeneous farms and farming systems. This paper proposes a categorisation of household diversity based on a functional typology of livelihood strategies, and analyses the influence of such diversity on current soil fertility status and spatial variability on a sample of 250 randomly selected farms from six districts of Kenya and Uganda. In spite of the agro-ecological and socio-economic diversity observed across the region (e.g. 4monthsyear−1 of food self-sufficiency in Vihiga, Kenya vs. 10 in Tororo, Uganda) consistent patterns of variability were also observed. For example, all the households with less than 3monthsyear−1 of food self-sufficiency had a land:labour ratio (LLR)<1, and all those with LLR>1 produced enough food to cover their diet for at least 5 months. Households with LLR<1 were also those who generated more than 50% of their total income outside the farm. Dependence on off/non-farm income was one of the main factors associated with household diversity. Based on indicators of resource endowment and income strategies and using principal component analysis, farmers’ rankings and cluster analysis the 250 households surveyed were grouped into five farm types: (1) Farms that rely mainly on permanent off-farm employment (from 10 to 28% of the farmers interviewed, according to site); (2) larger, wealthier farms growing cash crops (8–20%); (3) medium resource endowment, food self-sufficient farms (20–38%); (4) medium to low resource endowment relying partly on non-farm activities (18–30%); and (5) poor households with family members employed locally as agricultural labourers by wealthier farmers (13–25%). Due to differential soil management over long periods of time, and to ample diversity in resource endowments (land, livestock, labour) and access to cash, the five farm types exhibited different soil carbon and nutrient stocks (e.g. Type 2 farms had average C, N, P and K stocks that were 2–3 times larger than for Types 4 or 5). In general, soil spatial variability was larger in farms (and sites) with poorer soils and smaller in farms owning livestock. The five farm types identified may be seen as domains to target technological innovations and/or development efforts. [Copyright &y& Elsevier]

Abstract: In economic terms, resilience in farming has to do with the capacity of a farm business to survive various risks and other shocks. Despite its importance, resilience has seldom been directly considered in evaluations of economic sustainability. A whole-farm stochastic simulation model over a 6-year planning horizon was used to analyse organic and conventional cropping systems using a model of a representative farm in Eastern Norway. The relative economic sustainability of alternative systems under changing assumptions about future technology and price regimes was examined in terms of financial survival to the end of the planning period. The same alternatives were also compared in terms of stochastic efficiency. To model the risk of business failure adequately there is a need to deal with the risk of bankruptcy, and a modification of traditional analysis was used for that purpose. The organic farming system was found to be somewhat less economically sustainable than the conventional system, especially if the organic price premiums and the organic area payments were to be phased out. The results illustrate possible conflicts between pursuit of risk efficiency and economic sustainability. The model developed could be used to support farmers’ choices between farming systems as well as to help policy makers develop more sharply targeted policies. [Copyright &y& Elsevier]

Abstract: This work describes the analysis of the uncertainty linked to the annual direct and indirect losses of different nitrogenous compounds at the scale of a group of farms. The nitrogen (N) forms taken into account are: ammonia (NH3), nitric oxide (NO), nitrous oxide (N2O), dinitrogen (N2) and nitrate (NO3). The gaseous N emissions for the different components of the farms are estimated with a selection of adapted emission factors. The NO3 losses at the farm scale are calculated as the difference between the surplus of the farm-gate N balance and the gaseous N emissions. The feasibility of this method and the validity of the estimated NO3 losses have been demonstrated for a group of farms situated in the Naizin catchment area (Brittany, France) [Payraudeau, S., van der Werf, H.M.G., Vertès. F., 2006. Evaluation of an operational method for the estimation of emissions of nitrogen compounds for a group of farms. IJARGE 5 (2–3), 224–246]. Two sources of uncertainty were studied: (i) that associated with the terms of the farm-gate N balance and (ii) that associated with the different N emission factors. A Monte Carlo (MC) approach was used to assess the uncertainty associated with the calculated losses according to the uncertainty in the terms of the farm-gate N balance and in the emission factors used. The nitrogen losses are assessed, amongst others, to contribute to the establishment of an emissions inventory to be used in Life Cycle Assessment (LCA) of farming systems. Therefore, an analysis of the main problems limiting the implementation of the MC approach in LCA studies of agricultural production is proposed. The different stages in the implementation of the MC approach are described. The results reveal a greater sensitivity of emissions of NH3, N2O and N2 to the uncertainty associated with the emission factors than to that associated with the terms of the farm-gate N balance. The two sources of uncertainty contribute similarly to the uncertainty of NO3 losses. In spite of the occasionally large confidence intervals in the factors, the confidence intervals for the different direct losses are reasonably small. [Copyright &y& Elsevier]

There is a complex relationship between land-use, the environment, food production and quality. Regulatory and non-regulatory interventions, such as application of biosolids to agricultural land, are often undertaken at a field- or farm- level and the aggregated effects across farms and regions are difficult to understand, predict, and communicate to others both spatially and temporally. The aim of this paper was to introduce Waste to Land (W2L). W2L is a novel, holistic tool that combines spatial data analysis methods, multi-criteria analysis, and numerical models to detect areas for optimum biosolids application and to predict the spatial effect of applying biosolids to land. A robust framework was developed first to chart the effects of applied biosolids on the environment and, potentially, on the human food chain. Selecting cadmium as a prototype contaminant, a conceptual model was designed as the basis to define the workflow for the tool. Using the open-source, statistical language R and freely available data, the operation and outputs from the tool were demonstrated at a national scale in Scotland, UK. The results provided fresh intelligence on the ability of the Scottish Landbank to receive biosolids and on the spatial effect of landspreading biosolids, to facilitate the exchange of information between stakeholders, and to enable decision-making in a context of spreading biosolids on farmland. • A novel tool to predict the spatial effect of applying biosolids to land is proposed • The tool combines spatial data analysis, multi-criteria analysis, and numerical models in R • The tool's feasibility and potential was verified through stakeholder engagement • The tool will allow gaining new knowledge, enhancing communication among stakeholders, and aiding the decision-making process [ABSTRACT FROM AUTHOR]