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In this study, a farm-scale mathematical programming model for sheep and goat farms is proposed to simulate economic performance, including new resilience sheep traits that allow animals to counteract the presence of infectious and noninfectious diseases. The model was developed in the Small Ruminants Breeding for Efficiency and Resilience (SMARTER) Horizon 2020 project. The SMARTER model is a comprehensive and adaptable linear programming model that enables the assessment of hypothetical scenarios/challenges related to animal traits that prevent infectious and noninfectious diseases. The optimal performance and the structure of the farm are modeled under the presence of infectious and noninfectious diseases (disease plan) and under conditions where no diseases occur (future plan). A comparison of the model solutions, between presence and absence of diseases, provides suggested adjustments to the farming system and insights into the potential shape of new sustainable farm system profiles for the sheep and goat sector. Technical and economic data from five different sheep farms and one goat farm in Greece and France were used in this empirical application to assess different scenarios in the presence of mastitis, parasitism, and lameness in the flocks. The results showed that the profitability and sustainability of the farms are significantly improved when the resilience of animals reduces the impact of the diseases (the highest increase in gross margin was 23.5%). However, although there is substantial improvement in the economic performance of the farms that rear healthy animals, this does not affect the production and management plan of the farmer and does not alter the farm’s structure.

Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multi-method analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop-and region-specific adaptation strategies to ensure food security for an increasing world population.

Plants respond to resource stress by changing multiple aspects of their biomass allocation, morphology, physiology and architecture. To date, we lack an integrated view of the relative importance of these plastic responses in alleviating resource stress and of the consistency/variability of these responses among species. We subjected nine species (legumes, forbs and graminoids) to nitrogen and/or light shortages and measured 11 above-ground and below-ground trait adjustments critical in the alleviation of these stresses (plus several underlying traits). Nine traits out of 11 showed adjustments that improved plants’ potential capacity to acquire the limiting resource at a given time. Above ground, aspects of plasticity in allocation, morphology, physiology and architecture all appeared important in improving light capture, whereas below ground, plasticity in allocation and physiology were most critical to improving nitrogen acquisition. Six traits out of 11 showed substantial heterogeneity in species plasticity, with little structuration of these differences within trait covariation syndromes. Such comprehensive assessment of the complex nature of phenotypic responses of plants to multiple stress factors, and the comparison of plant responses across multiple species, makes a clear case for the high (but largely overlooked) diversity of potential plastic responses of plants, and for the need to explore the potential rules structuring them.

Crop pathogens and pests reduce the yield and quality of agricultural production. They cause substantial economic losses and reduce food security at household, national and global levels. Quantitative, standardized information on crop losses is difficult to compile and compare across crops, agroecosystems and regions. Here, we report on an expert-based assessment of crop health, and provide numerical estimates of yield losses on an individual pathogen and pest basis for five major crops globally and in food security hotspots. Our results document losses associated with 137 pathogens and pests associated with wheat, rice, maize, potato and soybean worldwide. Our yield loss (range) estimates at a global level and per hotspot for wheat (21.5% (10.1–28.1%)), rice (30.0% (24.6–40.9%)), maize (22.5% (19.5–41.1%)), potato (17.2% (8.1–21.0%)) and soybean (21.4% (11.0–32.4%)) suggest that the highest losses are associated with food-deficit regions with fast-growing populations, and frequently with emerging or re-emerging pests and diseases. Our assessment highlights differences in impacts among crop pathogens and pests and among food security hotspots. This analysis contributes critical information to prioritize crop health management to improve the sustainability of agroecosystems in delivering services to societies. An expert elicitation survey estimates yield losses for the five major food crops worldwide, suggesting that the highest losses are associated with food-deficit regions with fast-growing populations and frequently with emerging or re-emerging pests and diseases.

Background Global agriculture is undergoing a phase of agroecological transition. This transition will be characterized by adoption of agroecological cropping practices and by an increased diversity of soil management/tillage practices. However, very little is known as to whether or not crop seed germination and seedling emergence (hereafter referred to as SGE) will be affected under these cropping practices. Scope This paper first proposes a conceptual scheme which integrates key abiotic and biotic factors affecting crop SGE. Subsequently, the key mechanistic factors affecting SGE (i.e. intrinsic factors related to the seeds, and extrinsic factors related to the biotic and abiotic conditions of the seedbed), and how crop management practices can influence SGE through alterations of these direct mechanistic factors are discussed. This is done with special emphasis on how agricultural practices, particularly those related to agro-ecology, may impact SGE. Conclusions Crop SGE are affected by five major groups of drivers, namely seed and seedling characteristics, seedbed physical components, seedbed chemical components, seedbed biological components, and cropping systems. Although the crop SGE failure frequently occurs under field conditions, very little quantitative information is available in the literature on the real economic impact, the precise cause/s and ranking of factors associated with this failure. Re-seeding is often practiced for a number of crops to compensate the lack of SGE with significant direct and indirect costs for farmers. Little information exists in the literature concerning how SGE will be affected under agroecological cropping systems, such as conservation agriculture, or organic farming, or under climate-driven changes. Field observation, experimental and modeling studies are needed to fill the current knowledge gaps on the economic impact, precise cause/s and ranking of different stress factors associated with SGE failure.

Robots are heralded as part of a new revolution in agriculture. The agricultural robot’s capacity to reduce working time or improve working conditions is often advanced as a major contribution to sustainable agriculture. But the transformations of work appear to be more complex. Here we review the transformations of work subsequent to adoption of agricultural robots on the farm. We carry out a systematic review of literature using a multidisciplinary analytical framework of transformations of work. We consider four dimensions of work: 1) farm structure and the labor market; 2) work organization; 3) meaning of work; and 4) technical-economic performances. Given that the deployment of robots in agriculture is still in its early stages, most of the studies concern the automated milking system (AMS). The transformations of agricultural work represent an emerging topic dominated by a technical and economic vision of work. The major points are as follows: Concerning farm structures, we find no evidence of a relationship between robotization and a certain farm size. Concerning the labor market, there is a risk of skill- and wage-related segmentation and exclusion. The AMS is an example of an in-depth re-organization of work with a decrease in the physical workload but which is concomitant with a new mental workload of monitoring alarms. Concerning the meaning of work with AMS, the changes in the animal-human relationship lead to the possibility of new identities and new self-perceptions by farmers and workers. There is no evidence to support a reduction in working time when an AMS is installed. Finally, a synthesis of the results allows us to propose a research agenda that can better orient future research to understand the diversity of transformations of work resulting from the adoption of robots in agriculture.

In most current farming system classifications (e.g. \"conventional\"versus \"organic\"), each type of farming system encompasses a wide variety of farming practices and performances. Classifying farming systems using concepts such as \"ecological\", \"sustainable intensification\"or \"agro-ecology\"is not satisfactory because the concepts \"overlap in. definitions, principles and practices, thus creating. confusion in their meanings, interpretations and implications\". Existing classifications most often focus either on biotechnical functioning or on socio-economic contexts of farming systems. We reviewed the literature to develop an original analytical framework of the diversity of farming systems and agriculture models that deal with these limits. To describe this framework, we first present the main differences between three biotechnical types of farming systems differing in the role of ecosystem services and external inputs: chemical input-, biological input-and biodiversity-based farming systems. Second, we describe four key socio-economic contexts which determine development and functioning of these farming systems: globalised commodity-based food systems, circular economies, alternative food systems and integrated landscape approaches. Third, we present our original analytical framework of agriculture models, defined as biotechnical types of farming systems associated with one or a combination of socio-economic contexts differing in the role of relationships based on global market prices and \"territorial embeddedness\". We demonstrate the potential of this framework by describing six key agriculture models and reviewing key scientific issues in agronomy associated with each one. We then analyse the added value of our analytical framework and its generic character. Lastly, we discuss transversal research issues of the agriculture models, concerning the technologies required, their function in the bioeconomy, their multi-criteria and multi-level assessments, their co-existence and the transitions between them.

AbstractParadoxically, the number of crop–livestock farms is declining across Europe, despite the fact that crop-livestock farms are theoretically optimal to improve the sustainability of agriculture. To solve this issue, crop–livestock integration may be organized beyond the farm level. For instance, local groups of farmers can negotiate land-use allocation patterns and exchange materials such as manure, grain, and straw. Development of such a collective agricultural system raises questions, rarely documented in the literature, about how to integrate crops and livestock among farms, and the consequences, impacts, and conditions of integrating them. Here, we review the different forms of crop–livestock integration beyond the farm level, their potential benefits, and the features of decision support systems (DSS) needed for the integration process. We identify three forms of crop–livestock integration beyond the farm level: local coexistence, complementarity, and synergy, each with increasingly stronger temporal, spatial, and organizational coordination among farms. We claim that the forms of integration implemented define the nature, area, and spatial configuration of crops, grasslands, and animals in farms and landscapes. In turn, these configurations influence the provision of ecosystem services. For instance, we show that the synergy form of integration promotes soil fertility, erosion control, and field-level biological regulation services through organizational coordination among farmers and spatiotemporal integration between crops, grasslands, and animals. We found that social benefits of the synergy form of integration include collective empowerment of farmers. We claim that design of the complementarity and synergy forms of crop–livestock integration can best be supported by collective participatory workshops involving farmers, agricultural consultants, and researchers. In these workshops, spatialized simulation modeling of crop–livestock integration among farms is the basis for achieving the upscaling process involved in integrating beyond the farm level. Facilitators of these workshops have to pay attention to the consequences on governance and equity issues within farmers groups.

Integrated crop-livestock systems have been shown to improve nutrient cycling, particularly by re-coupling nitrogen and carbon cycles. Yet the number of mixed crop–livestock farms has been falling steadily in Europe. Integration between crops and livestock at the local level, through exchanges between already specialised farms, is rarely implemented. Given the lack of knowledge on new ways to maintain or to reintegrate crops and livestock from the farm up to the local level, concrete guidelines are needed. In this paper, we developed a transversal analysis of three complementary case studies regarding development of crop–livestock integration at the farm and beyond farm level. To this aim, we reviewed three French case studies in which participatory approaches were used to design scenarios of crop–livestock integration. When crop–livestock integration disappears from the farm level due to labour organisation, exchanges between specialised crop farmers and livestock farmers is a way to redevelop such integration at the local level. Transversal analysis of case-studies allowed us to suggest guidelines for further research regarding the design of agroecological crop–livestock integration. Articulating options of change at farm level and collective level allows to consider the appropriate level of design and trade-offs between (1) farm and beyond farm level, and (2) social, environmental and economic dimensions Considering these different levels of organisation is needed to identify possible pathways to and policy incentives for integrated crop–livestock systems. Developing specific Decision Support Systems and participative research is needed to conceive locally adapted scenarios of crop-livestock integration.