Smart Farming is a development that emphasizes the use of information and communication technology in the cyber-physical farm management cycle. New technologies such as the Internet of Things and Cloud Computing are expected to leverage this development and introduce more robots and artificial intelligence in farming. This is encompassed by the phenomenon of Big Data, massive volumes of data with a wide variety that can be captured, analysed and used for decision-making. This review aims to gain insight into the state-of-the-art of Big Data applications in Smart Farming and identify the related socio-economic challenges to be addressed. Following a structured approach, a conceptual framework for analysis was developed that can also be used for future studies on this topic. The review shows that the scope of Big Data applications in Smart Farming goes beyond primary production; it is influencing the entire food supply chain. Big data are being used to provide predictive insights in farming operations, drive real-time operational decisions, and redesign business processes for game-changing business models. Several authors therefore suggest that Big Data will cause major shifts in roles and power relations among different players in current food supply chain networks. The landscape of stakeholders exhibits an interesting game between powerful tech companies, venture capitalists and often small start-ups and new entrants. At the same time there are several public institutions that publish open data, under the condition that the privacy of persons must be guaranteed. The future of Smart Farming may unravel in a continuum of two extreme scenarios: 1) closed, proprietary systems in which the farmer is part of a highly integrated food supply chain or 2) open, collaborative systems in which the farmer and every other stakeholder in the chain network is flexible in choosing business partners as well for the technology as for the food production side. The further development of data and application infrastructures (platforms and standards) and their institutional embedment will play a crucial role in the battle between these scenarios. From a socio-economic perspective, the authors propose to give research priority to organizational issues concerning governance issues and suitable business models for data sharing in different supply chain scenarios.
► We analyzed 362 published organic–conventional comparative crop yields. ► The organic yield gap is 20%, but differs somewhat between crops and regions. ► We found a weak indication of an increasing yield gap as conventional yields increase. ► We hypothesize that when upscaling to farm/regional levels the yield gap will be larger. ► In that context, research is needed at farm and regional level and on nutrient availability. A key issue in the debate on the contribution of organic agriculture to the future of world agriculture is whether organic agriculture can produce sufficient food to feed the world. Comparisons of organic and conventional yields play a central role in this debate. We therefore compiled and analyzed a meta-dataset of 362 published organic–conventional comparative crop yields. Our results show that organic yields of individual crops are on average 80% of conventional yields, but variation is substantial (standard deviation 21%). In our dataset, the organic yield gap significantly differed between crop groups and regions. The analysis gave some support to our hypothesis that the organic–conventional yield gap increases as conventional yields increase, but this relationship was only rather weak. The rationale behind this hypothesis is that when conventional yields are high and relatively close to the potential or water-limited level, nutrient stress must, as per definition of the potential or water-limited yield levels, be low and pests and diseases well controlled, which are conditions more difficult to attain in organic agriculture. We discuss our findings in the context of the literature on this subject and address the issue of upscaling our results to higher system levels. Our analysis was at field and crop level. We hypothesize that due to challenges in the maintenance of nutrient availability in organic systems at crop rotation, farm and regional level, the average yield gap between conventional and organic systems may be larger than 20% at higher system levels. This relates in particular to the role of legumes in the rotation and the farming system, and to the availability of (organic) manure at the farm and regional levels. Future research should therefore focus on assessing the relative performance of both types of agriculture at higher system levels, i.e. the farm, regional and global system levels, and should in that context pay particular attention to nutrient availability in both organic and conventional agriculture.
Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the “next generation” models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.
Cropping systems that provide ecosystem services beyond crop production are gaining interest from farmers, policy makers and society at large, yet we lack frameworks to evaluate and manage for multiple ecosystem services. Using the example of integrating cover crops into annual crop rotations, we present an assessment framework that: (1) estimates the temporal dynamics of a suite of ecosystem services; (2) illustrates ecosystem multifunctionality using spider plots; and (3) identifies key time points for optimizing ecosystem service benefits and minimizing trade-offs. Using quantitative models and semi-quantitative estimates, we applied the framework to analyze the temporal dynamics of 11 ecosystem services and two economic metrics when cover crops are introduced into a 3-year soybean ( )–wheat ( )–corn ( ) rotation in a typical Mid-Atlantic climate. We estimated that cover crops could increase 8 of 11 ecosystem services without negatively influencing crop yields. We demonstrate that when we measure ecosystem services matters and cumulative assessments can be misleading due to the episodic nature of some services and the time sensitivity of management windows. For example, nutrient retention benefits occurred primarily during cover crop growth, weed suppression benefits occurred during cash crop growth through a cover crop legacy effect, and soil carbon benefits accrued slowly over decades. Uncertainties exist in estimating cover crop effects on several services, such as pest dynamics. Trade-offs occurred between cover crop ecosystem benefits, production costs, and management risks. Differences in production costs with and without cover crops varied 3-fold over 10 years, largely due to changes in fertilizer prices, and thus cover crop use will become more economical with increasing fertilizer prices or if modest cost-sharing programs are established. Frameworks such as that presented here provide the means to quantify ecosystem services and facilitate the transition to more multifunctional agricultural systems.
Due to the scarcity of alternative organic amendments, the retention of crop residue in fields can be considered key in promoting physical, chemical, and biological attributes of soil health in agricultural systems of developing countries. However, due to multiple other uses, small landholders in these countries are faced with trade-offs in managing crop residues. This article reviews crop residue management practices, mainly surface retention, incorporation or removal, describing their advantages and limitations in cereal-based agroecosystems in developing countries. The benefits of residue retention are regionally variable and depend on both agroclimatic and socioeconomic factors. Most studies from developing countries in Asia, Latin America, and Africa show positive effects of retaining crop residues on soil quality, soil organic matter and carbon storage, soil moisture retention, enhanced nutrient cycling, and decreased soil loss, among other environmental and soil health benefits. Variation was observed in the effect of surface retention vs. incorporation on various soil properties indicating the importance of taking into account abiotic factors such as climate, soil texture, study duration, sampling methods, and agronomic practices when assessing the impact of these practices. Negative effects of residue retention on crop performance attributed to nitrogen immobilization, waterlogging and decreased soil temperature have also been reported in some environments. Residue trade-offs in mixed crop-livestock systems in developing countries can limit the amount of residue retained. However, interventions such as intensification, partial retention, improved return of nutrients from manures, and the provision of substitutes to the current functions of livestock (e.g. mechanization, insurance) could reduce these residue trade-offs in favour of promoting long-term soil health.
The purpose of this article is to investigate effective reformism: strategies that innovation networks deploy to create changes in their environment in order to establish a more conducive context for the realization and durable embedding of their innovation projects. Using a case study approach, effective reformism efforts are analyzed in a technological innovation trajectory related to the implementation of a new poultry husbandry system and an organizational innovation trajectory concerning new ways of co-operation among individual farms to establish economies of scale. The findings reinforce the idea, emerging from a complexity perspective on agricultural innovation systems, that interaction between innovation networks and their environment is only steerable to a limited extent. Nonetheless, innovation networks can enhance effective reformism by creating tangible visions that serve as vehicles to create understanding about the innovation and mobilize support for it, and by employing several kinds of boundary spanning individuals that are able to forge effective connections between innovation networks and their environment. Because innovation networks can only partially influence their institutional environment, and because unintended consequences of actions and random events influence the course of the innovation process, innovation network actors need to continuously re-interpret the contexts in which they move. This constant reflection by the innovating actors on their position vis-à-vis their environment needs to be supported by dedicated facilitators and monitoring and evaluation methods aimed at system learning. This implies that agricultural innovation policies should, instead of aiming to fully plan and control innovation, foster the emergence of such flexible support instruments that enable adaptive innovation management.
Decision support tools, usually considered to be software-based, may be an important part of the quest for evidence-based decision-making in agriculture to improve productivity and environmental outputs. These tools can lead users through clear steps and suggest optimal decision paths or may act more as information sources to improve the evidence base for decisions. Yet, despite their availability in a wide range of formats, studies in several countries have shown uptake to be disappointingly low. This paper uses a mixed methods approach to investigate the factors affecting the uptake and use of decision support tools by farmers and advisers in the UK. Through a combination of qualitative interviews and quantitative surveys, we found that fifteen factors are influential in convincing farmers and advisers to use decision support tools, which include usability, cost-effectiveness, performance, relevance to user, and compatibility with compliance demands. This study finds a plethora of agricultural decision support tools in operation in the UK, yet, like other studies, shows that their uptake is low. A better understanding of the fifteen factors identified should lead to more effective design and delivery of tools in the future.
We review the current state of agricultural systems science, focusing in particular on the capabilities and limitations of agricultural systems models. We discuss the state of models relative to five different Use Cases spanning field, farm, landscape, regional, and global spatial scales and engaging questions in past, current, and future time periods. Contributions from multiple disciplines have made major advances relevant to a wide range of agricultural system model applications at various spatial and temporal scales. Although current agricultural systems models have features that are needed for the Use Cases, we found that all of them have limitations and need to be improved. We identified common limitations across all Use Cases, namely 1) a scarcity of data for developing, evaluating, and applying agricultural system models and 2) inadequate knowledge systems that effectively communicate model results to society. We argue that these limitations are greater obstacles to progress than gaps in conceptual theory or available methods for using system models. New initiatives on open data show promise for addressing the data problem, but there also needs to be a cultural change among agricultural researchers to ensure that data for addressing the range of Use Cases are available for future model improvements and applications. We conclude that multiple platforms and multiple models are needed for model applications for different purposes. The Use Cases provide a useful framework for considering capabilities and limitations of existing models and data.
Despite the importance of livestock to poor people and the magnitude of the changes that are likely to befall livestock systems, the intersection of climate change and livestock in developing countries is a relatively neglected research area. Little is known about the interactions of climate and increasing climate variability with other drivers of change in livestock systems and in broader development trends. In many places in the tropics and subtropics, livestock systems are changing rapidly, and the spatial heterogeneity of household response to change may be very large. While opportunities may exist for some households to take advantage of more conducive rangeland and cropping conditions, for example, the changes projected will pose serious problems for many other households. We briefly review the literature on climate change impacts on livestock and livestock systems in developing countries, and identify some key knowledge and data gaps. We also list some of the broad researchable issues associated with how smallholders and pastoralists might respond to climate change. The agendas of research and development organisations may need adjustment if the needs of vulnerable livestock keepers in the coming decades are to be met effectively.
Losses at every stage in the food system influence the extent to which nutritional requirements of a growing global population can be sustainably met. Inefficiencies and losses in agricultural production and consumer behaviour all play a role. This paper aims to understand better the magnitude of different losses and to provide insights into how these influence overall food system efficiency. We take a systems view from primary production of agricultural biomass through to human food requirements and consumption. Quantities and losses over ten stages are calculated and compared in terms of dry mass, wet mass, protein and energy. The comparison reveals significant differences between these measurements, and the potential for wet mass figures used in previous studies to be misleading. The results suggest that due to cumulative losses, the proportion of global agricultural dry biomass consumed as food is just 6% (9.0% for energy and 7.6% for protein), and 24.8% of harvest biomass (31.9% for energy and 27.8% for protein). The highest rates of loss are associated with livestock production, although the largest absolute losses of biomass occur prior to harvest. Losses of harvested crops were also found to be substantial, with 44.0% of crop dry matter (36.9% of energy and 50.1% of protein) lost prior to human consumption. If human over-consumption, defined as food consumption in excess of nutritional requirements, is included as an additional inefficiency, 48.4% of harvested crops were found to be lost (53.2% of energy and 42.3% of protein). Over-eating was found to be at least as large a contributor to food system losses as consumer food waste. The findings suggest that influencing consumer behaviour, e.g. to eat less animal products, or to reduce per capita consumption closer to nutrient requirements, offer substantial potential to improve food security for the rising global population in a sustainable manner.
African farming systems are highly heterogeneous: between agroecological and socioeconomic environments, in the wide variability in farmers’ resource endowments and in farm management. This means that single solutions (or ‘silver bullets’) for improving farm productivity do not exist. Yet to date few approaches to understand constraints and explore options for change have tackled the bewildering complexity of African farming systems. In this paper we describe the Nutrient Use in Animal and Cropping systems – Efficiencies and Scales (NUANCES) framework. NUANCES offers a structured approach to unravel and understand the complexity of African farming to identify what we term ‘best-fit’ technologies – technologies targeted to specific types of farmers and to specific niches within their farms. The NUANCES framework is not ‘just another computer model’! We combine the tools of systems analysis and experimentation, detailed field observations and surveys, incorporate expert knowledge (local knowledge and results of research), generate databases, and apply simulation models to analyse performance of farms, and the impacts of introducing new technologies. We have analysed and described complexity of farming systems, their external drivers and some of the mechanisms that result in (in)efficient use of scarce resources. Studying sites across sub-Saharan Africa has provided insights in the trajectories of change in farming systems in response to population growth, economic conditions and climate variability (cycles of drier and wetter years) and climate change. In regions where human population is dense and land scarce, farm typologies have proven useful to target technologies between farmers of different production objectives and resource endowment (notably in terms of land, labour and capacity for investment). In such regions we could categorise types of fields on the basis of their responsiveness to soil improving technologies along soil fertility gradients, relying on local indicators to differentiate those that may be managed through ‘maintenance fertilization’ from fields that are highly-responsive to fertilizers and fields that require rehabilitation before yields can improved. Where human population pressure on the land is less intense, farm and field types are harder to discern, without clear patterns. Nutrient cycling through livestock is in principle not efficient for increasing food production due to increased nutrient losses, but is attractive for farmers due to the multiple functions of livestock. We identified trade-offs between income generation, soil conservation and community agreements through optimising concurrent objectives at farm and village levels. These examples show that future analyses must focus at farm and farming system level and not at the level of individual fields to achieve appropriate targeting of technologies – both between locations and between farms at any given location. The approach for integrated assessment described here can be used to explore the potential of best-fit technologies and the ways they can be best combined at farm level. The dynamic and integrated nature of the framework allows the impact of changes in external drivers such as climate change or development policy to be analysed. Fundamental questions for integrated analysis relate to the site-specific knowledge and the simplification of processes required to integrate and move from one level to the next.
► Increasing the productivity of African smallholders is key to global food security. ► Institutions explain much variance in the quantity and quality of smallholder output. ► The article reports on innovation system approaches to institutional change. ► It reviews the literature on institutional change, innovation systems and platforms. ► It presents experience of a 5-year research programme in Benin, Ghana and Mali. Sustainable intensification of smallholder farming is a serious option for satisfying 2050 global cereal requirements and alleviating persistent poverty. That option seems far off for Sub-Sahara Africa (SSA) where technology-driven productivity growth has largely failed. The article revisits this issue from a number of angles: current approaches to enlisting SSA smallholders in agricultural development; the history of the phenomenal productivity growth in the USA, The Netherlands and Green Revolution Asia; and the current framework conditions for SSA productivity growth. This analysis shows that (1) the development of an enabling institutional context was a necessary condition that preceded the phenomenal productivity growth in industrial and Green Revolution countries; and that (2) such a context is also present for successful SSA export crop production, but that (3) the context is pervasively biased against SSA’s smallholder food production. The article traces the origins of technology supply push (TSP) as a dominant paradigm that hinders recognition of the role of enabling institutions. The article then reviews the literature on institutional change and zooms in on Innovation Platforms (IPs) as a promising innovation system approach to such change. We describe the concrete experience with IP in the Sub-Sahara Challenge Program (SSA-CP) and in the Convergence of Sciences: Strengthening Innovation Systems (CoS-SIS) Program. The former has demonstrated proof of concept. The latter is designed to trace causal mechanisms. We describe its institutional experimentation and research methodology, including causal process tracing.
Global grain production has increased dramatically during the past 50 years, mainly as a consequence of intensified land management and introduction of new technologies. For the future, a strong increase in grain demand is expected, which may be fulfilled by further agricultural intensification rather than expansion of agricultural area. Little is known, however, about the global potential for intensification and its constraints. In the presented study, we analyze to what extent the available spatially explicit global biophysical and land management-related data are able to explain the yield gap of global grain production. We combined an econometric approach with spatial analysis to explore the maximum attainable yield, yield gap, and efficiencies of wheat, maize, and rice production. Results show that the actual grain yield in some regions is already approximating its maximum possible yields while other regions show large yield gaps and therefore tentative larger potential for intensification. Differences in grain production efficiencies are significantly correlated with irrigation, accessibility, market influence, agricultural labor, and slope. Results of regional analysis show, however, that the individual contribution of these factors to explaining production efficiencies strongly varies between world-regions.
The agricultural innovation systems approach emphasizes the collective nature of innovation and stresses that innovation is a co-evolutionary process, resulting from alignment of technical, social, institutional and organizational dimensions. These insights are increasingly informing interventions that focus on setting up multi-stakeholder initiatives, such as innovation platforms and networks, as mechanisms for enhancing agricultural innovation, particularly in sub-Saharan Africa. There has been much emphasis on how such platforms are organized, but only limited analysis unravelling how they shape co-evolution of innovation processes. This paper addresses this gap and conceptualizes platforms as intermediaries that connect the different actors in innovation systems in order to foster effective co-evolution. We present a case study of a smallholder dairy development programme in Kenya, led by a consortium of five organizations that provide a platform for building multi-actor partnerships to enhance smallholder dairy productivity and improve livelihoods. The findings indicate that co-evolution of innovation is a highly dynamic process with various interactional tensions and unexpected effects, and that the distributed nature of intermediation is important in resolving some of these tensions emerging at different actor interfaces. However, platforms are not always able to adapt adequately to emerging issues. This points to the need to look at platforms dynamically and pay more attention to mechanisms that strengthen feedback, learning and adaptive management in innovation processes.
► FAO projections imply that global agricultural area may expand by 280 Mha in 2030. ► Faster growth in livestock productivity may decrease global area by 230 Mha in 2030. ► 20% substitution of ruminant meat may decrease area by an additional 480 Mha. Growing global population figures and per-capita incomes imply an increase in food demand and pressure to expand agricultural land. Agricultural expansion into natural ecosystems affects biodiversity and leads to substantial carbon dioxide emissions. Considerable attention has been paid to prospects for increasing food availability, and limiting agricultural expansion, through higher yields on cropland. In contrast, prospects for efficiency improvements in the entire food-chain and dietary changes toward less land-demanding food have not been explored as extensively. In this study, we present model-based scenarios of global agricultural land use in 2030, as a basis for investigating the potential for land-minimized growth of world food supply through: (i) faster growth in feed-to-food efficiency in animal food production; (ii) decreased food wastage; and (iii) dietary changes in favor of vegetable food and less land-demanding meat. The scenarios are based in part on projections of global food agriculture for 2030 by the Food and Agriculture Organization of the United Nations, FAO. The scenario calculations were carried out by means of a physical model of the global food and agriculture system that calculates the land area and crops/pasture production necessary to provide for a given level of food consumption. In the reference scenario – developed to represent the FAO projections – global agricultural area expands from the current 5.1 billion ha to 5.4 billion ha in 2030. In the faster-yet-feasible livestock productivity growth scenario, global agricultural land use decreases to 4.8 billion ha. In a third scenario, combining the higher productivity growth with a substitution of pork and/or poultry for 20% of ruminant meat, land use drops further, to 4.4 billion ha. In a fourth scenario, applied mainly to high-income regions, that assumes a minor transition towards vegetarian food (25% decrease in meat consumption) and a somewhat lower food wastage rate, land use in these regions decreases further, by about 15%.
We used ISO-compliant life cycle assessment (LCA) to compare the cumulative energy use, ecological footprint, greenhouse gas emissions and eutrophying emissions associated with models of three beef production strategies as currently practiced in the Upper Midwestern United States. Specifically we examined systems where calves were either: weaned directly to feedlots; weaned to out-of-state wheat pastures (backgrounded) then finished in feedlots; or finished wholly on managed pasture and hay. Impacts per live-weight kg of beef produced were highest for pasture-finished beef for all impact categories and lowest for feedlot-finished beef, assuming equilibrium conditions in soil organic carbon fluxes across systems. A sensitivity analysis indicated the possibility of substantial reductions in net greenhouse gas emissions for pasture systems under conditions of positive soil organic carbon sequestration potential. Forage utilization rates were also found to have a modest influence on impact levels in pasture-based beef production. Three measures of resource use efficiency were applied and indicated that beef production, whether feedlot or pasture-based, generates lower edible resource returns on material/energy investment relative to other food production strategies.
Agricultural systems continuously evolve and are forced to change as a result of a range of global and local driving forces. Agricultural technologies and agricultural, environmental and rural development policies are increasingly designed to contribute to the sustainability of agricultural systems and to enhance contributions of agricultural systems to sustainable development at large. The effectiveness and efficiency of such policies and technological developments in realizing desired contributions could be greatly enhanced if the quality of their ex-ante assessments were improved. Four key challenges and requirements to make research tools more useful for integrated assessment in the European Union were defined in interactions between scientists and the European Commission (EC), i.e., overcoming the gap between micro–macro level analysis, the bias in integrated assessments towards either economic or environmental issues, the poor re-use of models and hindrances in technical linkage of models. Tools for integrated assessment must have multi-scale capabilities and preferably be generic and flexible such that they can deal with a broad variety of policy questions. At the same time, to be useful for scientists, the framework must facilitate state-of-the-art science both on aspects of the agricultural systems and on integration. This paper presents the rationale, design and illustration of a component-based framework for agricultural systems (SEAMLESS Integrated Framework) to assess, ex-ante, agricultural and agri-environmental policies and technologies across a range of scales, from field–farm to region and European Union, as well as some global interactions. We have opted for a framework to link individual model and data components and a software infrastructure that allows a flexible (re-)use and linkage of components. The paper outlines the software infrastructure, indicators and model and data components. The illustrative example assesses effects of a trade liberalisation proposal on EU’s agriculture and indicates how SEAMLESS addresses the four identified challenges for integrated assessment tools, i.e., linking micro and macro analysis, assessing economic, environmental, social and institutional indicators, (re-)using standalone model components for field, farm and market analysis and their conceptual and technical linkage.
A life cycle assessment (LCA) was conducted to estimate whole-farm greenhouse gas (GHG) emissions from beef production in western Canada. The aim was to determine the relative contributions of the cow–calf and feedlot components to these emissions, and to examine the proportion of whole-farm emissions attributable to enteric methane (CH ). The simulated farm consisted of a beef production operation comprised of 120 cows, four bulls, and their progeny, with the progeny fattened in a feedlot. The farm also included cropland and native prairie pasture for grazing to supply the feed for the animals. The LCA was conducted over 8 years to fully account for the lifetime GHG emissions from the cows, bulls and progeny, as well as the beef marketed from cull cows, cull bulls, and progeny raised for market. The emissions were estimated using Holos, a whole-farm model developed by Agriculture and Agri-Food Canada. Holos is an empirical model, with a yearly time-step, based on the Intergovernmental Panel on Climate Change methodology, modified for Canadian conditions and farm scale. The model considers all significant CH , N O, and CO emissions and removals on the farm, as well as emissions from manufacture of inputs (fertilizer, herbicides) and off-farm emissions of N O derived from nitrogen applied on the farm. The LCA estimated the GHG intensity of beef production in this system at 22 kg CO equivalent (kg carcass) . Enteric CH was the largest contributing source of GHG accounting for 63% of total emissions. Nitrous oxide from soil and manure accounted for a further 27% of the total emissions, while CH emissions from manure and CO energy emissions were minor contributors. Within the beef production cycle, the cow–calf system accounted for about 80% of total GHG emissions and the feedlot system for only 20%. About 84% of enteric CH was from the cow–calf herd, mostly from mature cows. It follows that mitigation practices to reduce GHG emissions from beef production should focus on reducing enteric CH production from mature beef cows. However, mitigation approaches must also recognize that the cow–calf production system also has many ancillary environmental benefits, allowing use of grazing and forage lands that can preserve soil carbon reserves and provide other ecosystems services.
► Widespread cropland abandonment occurred in Albania and Romania since 1990. ► We analyzed cropland abandonment using boosted regression trees (BRTs). ► Our analysis allows rigorous country comparison over time and across space. ► Topography determines a large share of variation in abandonment patterns. ► Abandonment patterns are shaped by increasing market orientation of agriculture. The collapse of socialist governance structures in Central and Eastern Europe led to the widespread abandonment of agricultural land. We estimated and compared the determinants of cropland abandonment in Albania and Romania during the postsocialist transitional period from 1990 to 2005. The data set included cropland abandonment derived from satellite image analysis, spatially continuous biogeophysical indicators, and socioeconomic surveys. Data were analyzed using boosted regression trees. Boosted regression trees can account for nonlinearities and interactions between variables and combine high predictive accuracy with appealing options to interpret the results. The results revealed important similarities between cropland abandonment in the countries and showed a strong correlation of abandonment with elevation and slope. Differences between cropland abandonment in Albania and Romania were apparent when the influence of topography was excluded. While physical accessibility tended to be more important in Albania, the density of cropland and input intensity were more decisive in Romania. The immediate time period following the collapse of socialism was dominated by extensive cropland abandonment in areas where agricultural production was no longer profitable. Gradual changes were observed in later stages of the transition period.
► Organic farming had similar or lower environmental impacts than integrated production. ► Organic farming used less resources, except land. ► Organic farming had higher biodiversity potential and lower ecotoxicity. ► Weak points of organic farming: lower yields and nutrient losses. Organic farming (OF) is considered a promising solution for reducing environmental burdens related to intensive agricultural management practices. The question arises whether OF really reduces the environmental impacts once lower yields and all the changes in farming methods are taken into consideration. This question is addressed in a comprehensive study of Swiss arable cropping and forage production systems comparing OF to integrated production (IP) systems by means of the life cycle assessment (LCA) method. The LCA study investigated the environmental impacts of two long-term farming system experiments: the DOC experiment comparing bio-dynamic, bio-organic and conventional/integrated farming and the “Burgrain” experiment encompassing integrated intensive, integrated extensive and organic production. All treatments received similar amounts of farmyard manure. The system boundary encompasses the plant production system; storage and application of farmyard manure is included in the system boundary, the animal husbandry is not included. The Swiss Agricultural Life Cycle Assessment method (SALCA) was used to analyse the environmental impacts. In the overall assessment OF was revealed to be either superior or similar to IP in environmental terms. OF has its main strengths in better resource conservation, since the farming system relies mainly on farm-internal resources and limits the input of external auxiliary materials. This results in less fossil and mineral resources being consumed. Moreover the greatly restricted use of pesticides makes it possible to markedly reduce ecotoxicity potentials on the one hand, and to achieve a higher biodiversity potential on the other. This overall positive assessment is not valid for all organic products: some products such as potatoes had higher environmental burdens than their counterparts from IP. The main drawbacks identified for Swiss OF systems are lower yields. As a consequence some production factors are used less efficiently, thus partly negating the advantages of OF. Furthermore, the different manure management strategy leads to relatively high nutrient losses in relation to yield. These two points were shown to be the main priorities for the environmental optimisation of OF systems. The differences between the bio-organic and the bio-dynamic farming systems consisted in a slightly higher input of organic matter, a few applications of mineral fertilisers and copper applications in the former. The eco-efficiency analysis led to the conclusion that the optimisation of OF is mainly output-driven, i.e. that higher yields of good quality should be achieved with the available (limited) resources. On the contrary, optimisation of IP was found to be input-driven; the inputs should be used in a quantity and manner which minimise the environmental burdens per unit produced. The study showed that despite the efforts of recent years, there is still considerable room for the environmental optimisation of Swiss farming systems.