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Agri-food systems in rural areas are increasingly dependent on collective and participatory actions to preserve traditional production and local agro-ecosystems. The deployment of digital technologies is expected to play a role in this context, provided that an integrated use of these tools is pursued. The paper analyses the characteristics of an integrated strategy of digitalisation led by a collective organisation, integrating the perspective of the Socio-Cyber-Physical System (SCPS) in the agri-food system. The aim is to reveal how the digitalisation process shapes and impacts on the governance of collective actions in a scenario of ecological transition. This case study examines the production system and Consortium for the protection of PDO Parmigiano Reggiano cheese, using the Institutional Analysis and Development framework. Evidence from official documents, direct observations, semi-structured interviews, and focus group are used to identify action situations and their connections. The paper allows us to identify a network of applications of digital tools, adopted in different business areas of a complex organisation such as the Parmigiano Reggiano Consortium and its production system. This network of integrated business functions can be seen as the hallmark of the digitalisation strategy elaborated by this Consortium at the intersection with the surrounding social and physical domains, strengthening the embeddedness in the PR production system. However, some obstacles and drawbacks of this strategy also emerged, such as a marginal integration with the ecological transition. This research makes a valuable contribution to the process of identifying the fundamental elements of the digital network that represents the digitalisation strategy of the Parmigiano Reggiano production system. It is important to address any existing limitations and emerging conflicts in order to guarantee effective quality monitoring and traceability of the cheese without any prejudice for the natural resources of the PR production area. This must be done in a way that does not compromise the distinctive production processes that are deeply rooted in local traditions and require specialised, labour-intensive techniques. [Display omitted] • Agri-food systems increasingly rely on digital technologies for their governance in a scenario of ecological transition • The IAD framework allows to explore digitalisation strategy of collective actions in farming systems • We identify a network of action situations in the case of the Parmigiano Reggiano Consortium, representing its digitalisation strategy • The integrated use of digital tools strengthens the embeddedness of stakeholders in the production system • The twin transition of collective actions needs proper governance to enhance distinctive agri-food products [ABSTRACT FROM AUTHOR]

There is growing evidence that diversified cropping systems can contribute to the fundamental reorientation of food production. However, actors involved in crop diversification (CD) initiatives often lack concrete action perspectives, i.e., ideas and expectations on what they can do to achieve their goals. Indeed, various authors have pointed out the lack of operational guidance by high-level theories on innovation governance for actors in innovation niches that have to cope with complexity and unpredictability. This paper aims to fill this gap by studying and cross-analysing the enactment of 25 Case Studies (CSs) consisting of multi-actor CD initiatives spread across 10 European countries. We developed a heuristic framework that aimed to unpack the key components in the CSs' ways of working towards CD (motivations, participants, intervention levels, activities and learnings). Data collection was based on reflexive self-assessment documents produced by the CSs as part of the actor-oriented co-innovation approach. We cross-analysed the data to highlight patterns among the CSs in their ways of working towards CD using both qualitative and quantitative (Multiple Factor Analysis) approaches, in order to provide a diversity of insights to support action perspectives. Seven groups of CSs were defined and qualified through the qualitative approach. Group 1 worked on fostering co-learning between farmers and developing participatory approaches. Group 2 specifically worked on enhancing cooperation between farmers. Group 3 promoted strip cropping through the development of a community of practice. Group 4 was concerned with gradually removing lock-ins to support the introduction of diversifying crops. Group 5 worked at aligning actors to create value chains to enhance grain legume production. Group 6 was about fostering locally integrated food systems for legumes and vegetables. And, Group 7 gathered CSs searching for ways out of wicked situations. The results from the MFA partially confirmed these similarities between the CSs but also revealed major differences between some CSs that were identified as similar with the qualitative approach. In this paper, we propose a framework to describe how actors within innovation niches organized themselves, worked and learned together to stimulate a transition towards more sustainability in food systems. We assume that this framework, is usable by other project leaders of innovation niches to monitor and analyse their change processes towards sustainability jointly with practitioners. Grouping the CSs is a way to scale out these learnings and contribute to the production of action perspectives and mobilizing impact that are useful for people both inside and outside our project. [Display omitted] • Actors involved in crop diversification initiatives often lack of action perspectives to manage complexity and uncertainty. • The enactment of crop diversification in 25 innovations niches was studied using a quali-quantitative comparative approach. • Seven groups were defined through a qualitative approach and then tempered based a the Multi-Factor Analysis. • This analysis contributed to reduce task uncertainty within the case studies of the project by informing social learning. • By making explicit the processes and by comparing them, we provided guidance to actors in coping with complex situations. [ABSTRACT FROM AUTHOR]

Over the past two decades, the Mekong region has experienced significant transformation of its agricultural sector from subsistence farming to export crops driven by the expansion of agricultural land and of irrigation, plus intensification thanks to mechanization and the use of chemical inputs. In the context of agrarian transition, maintaining "organic by default" rice farming systems that do not rely on chemical inputs, is challenging. Based on a case study in Preah Vihear, the organic province of Cambodia, this paper examines whether organic rice certification can maintain "organic by default" practices in a context of unprecedented agricultural intensification. Using agrarian diagnosis, we investigated the impacts of an organic rice certification scheme on the future of organic rice-based farming systems. We built a functional typology of five farming systems that co-exist in the study area to understand the roles of organic rice in each system. From an economic perspective, organic rice is less profitable than recently introduced cash crops, e.g. cassava, cashews. The positive impact of certification of organic rice is threatened by lock-in factors, e.g. labor diversification in both farming and non-farming activities, cash flow management, the certification process, leading farmers to abandon organic rice for cash crops for export. We discuss the key role of agricultural cooperatives in the certification process of organic rice. The paper questions the capacity of a certification scheme to keep up with the process of intensification that undermines the profitability of the remaining "organic by default" rice systems. Beyond the significance of the results in the study area, one of the last strongholds of traditional rice systems in Cambodia, the paper illustrates the disappearance of "organic by default" rice systems in the whole country as well as in the Mekong region. [Display omitted] • The role of organic rice was analyzed in five co-existing rice-based farming systems. • Organic rice is threatened by intensified production in lowland areas. • Other lock-in factors in upland areas jeopardize the cultivation of "organic by default" rice. • Organic certification is complex, hence the progressive abandonment of organic rice. [ABSTRACT FROM AUTHOR]

Organizations often use agri-environmental policies and program to promote Best Management Practice (BMPs) adoption, which are meant to mitigate on-farm environmental risks. However, producer preferences for programs and collaborative organizations are often overlooked during the process of policy implementation. Producers are likely to feel more inclined to accept information from, and otherwise engage with, some organizations over others. Overlooking such preferences may have led to inefficient policies that did not engage the intended audience, resulting in minimal BMP uptake. The goal of this paper is to investigate the preferences of Canadian producers for climate-mitigative policy options and partner organizations that impact or encourage BMP adoption, two variables deemed critical to on-farm BMP adoption decisions. We use a survey-based method for assessing individuals' priorities in the form of most and least preferred choices from a set of available options. Our analysis reveals that producers prefer financial support, while certification, extension, carbon offsets and carbon taxation are all significantly less likely to be chosen as the most preferred climate policy. As for organizations producers prefer to work with, the provincial government and county or municipal governments are their first-choice collaborative organizations when it comes to climate-mitigation in agriculture. In contrast, producers in Alberta are more likely to choose environmental NGOs as their least preferred organization. The results also show these preferences are altered when interacted with producer characteristics. Beliefs about climate change significantly alter their policy preferences: those who believe in human-caused (anthropogenic) climate change are more likely to support several climate-mitigative policies, with the notable exception of a tax on carbon and carbon offsets. We also find farm typology and being an avid adopter of BMPs alters producers' policy preferences, but not organization preferences. Given the challenges to changing producers' on-farm decision making, the results of this paper can help guide those who develop and implement climate-mitigative policies and programs by providing an overview of preferred policies that are more likely to be accepted by agricultural producers. This includes helping tailor policy approaches in collaboration with trusted partners and understanding what factors, such as farm typology and climate change beliefs, may influence the acceptance of these climate-mitigative policies. [Display omitted] • Farmers' policy and organization preferences shape adoption of proven climate-mitigative BMPs. • Male and strongly conservative farmers unanimously oppose carbon taxation. • Farmers hold differing views of organizations in the agri-environmental space. Messenger effects matter. • Farmer are largely unwilling to work with environmental organizations on climate BMP adoption. • Farmers stated climate beliefs significantly affect their policy preferences, except for carbon taxation. [ABSTRACT FROM AUTHOR]

Global food systems are confronted with multifaceted challenges, including climate change, malnutrition, and biodiversity loss, disproportionately affecting the livelihoods of millions, particularly in low- and middle-income countries. Recognizing its potential to address these challenges, innovation is increasingly central in public sector organizations. However, despite growing emphasis, the adoption of innovation portfolio management approaches remains limited. Drawing on a case study of CGIAR, a global research partnership dedicated to agricultural challenges, this paper seeks to accomplish two objectives. The primary objective is to produce a set of best practices in innovation portfolio management that can benefit public sector organizations. The secondary objective is to demonstrate how this offers an important precursor to guide organizational decision-making and investment towards responsible food system transformation. An in-depth analysis of CGIAR's approach to innovation portfolio management is based on qualitative and quantitative data from sources such as interviews, observations, documents, and archival records. The study adopts a descriptive case study approach, utilizing an analytical framework that recognizes the importance of methods, mindsets, and mechanisms in describing and analyzing the CGIAR case. Key lessons encompass the need for a holistic perspective on innovation portfolio management. The paper advocates for the integration of an innovation portfolio management approach within broader (1) organizational processes, including human resources, finance, legal frameworks, and risk management; and (2) innovation systems where food system transformation is the outcome of interactions and partnerships between government, public and private sector organizations. This study furnishes actionable guidance to public organizations seeking to harness innovation portfolio management for responsible and impactful food system transformation. [Display omitted] • Innovation portfolio management is underutilized in the public research and development sector. • CGIAR is mainstreaming an innovation portfolio management approach based on Scaling Readiness principles. • The objective is to enhance CGIAR's effectiveness and performance in achieving its 2030 research and innovation strategy. • Innovation portfolio management necessitates a holistic approach with investment in methods, mindsets, and mechanisms. • Integrating this approach into decision-making and program cycles serves as a significant incentive for its adoption and success. [ABSTRACT FROM AUTHOR]

The complexity of farming and rural communities poses challenges to research and extension initiatives seeking industry-wide change. The effectiveness of these initiatives depends on factors ranging from individual psychology to the science of effects. Tools like intervention logic models, which visualise the causal chain of an intervention through to its impact, are useful for programme planning but are of limited usefulness for comparing the relative benefits of initiatives. Benefit-cost analysis can quantify relative benefits, but applications often oversimplify the causal chain and leave key impacts unquantified. This paper aims to develop a benefit-cost analysis modelling framework that both captures the important causal logic from intervention to impact as well as quantifying and monetising key benefit categories. We apply the framework to value the impacts of 'Hill Country Futures', a programme of research and extension designed to assist in future-proofing the environmental sustainability, profitability, and well-being of New Zealand's hill country farmers, their farm systems, and communities. The QUICK (Quantifying and Understanding the Impact of Capability and Knowledge) model uses an agent-based simulation to represent processes of skill development and practice improvement that result from both extension activities for management and research tools as well as the use of those tools. We calibrate the model using a combination of an external predictive adoption model, an expert workshop, and researcher judgment. We use a choice experiment to value simulated changes in financial, environmental, community, and well-being outcomes. We explore how optimising the programme of extension interventions through targeting could increase impact. Our results suggest that the benefits of the 'Hill Country Futures' programme outweigh the costs by a factor of 13.5. We find that targeting extension efforts towards building awareness could be slightly more beneficial than targeting both awareness and skill development. We find no evidence that targeting extension efforts towards the best resource would be beneficial. This paper combines desirable features from intervention logic models, benefit-cost analysis, choice modelling, and agent-based models to value the benefits of a large programme of research and extension. It highlights that such modelling can be useful for evaluating both planned and complete programmes. [Display omitted] • Programme evaluation is dominated by qualitative research and would often benefit from more quantitative analysis. • We evaluate Hill Country Futures, designed to improve the sustainability and profitability of hill country farmers. • We combine intervention logic models with benefit-cost analysis using an agent-based approach informed by choice modelling. • Our results suggest that the programme will deliver substantial benefits compared to costs, with a benefit-cost ratio of 13.5. • We show the potential of using intervention logic with benefit-cost analysis to value agricultural research and extension. [ABSTRACT FROM AUTHOR]

Peanuts (Arachis hypogaea) hold significant global economic and nutritional importance, positioning them as a crucial crop in the realm of sustainable agriculture. In this comprehensive systematic and meta-analytic review, we thoroughly examine the extensive research and innovative strategies related to mechanizing peanut farming, with a specific focus on activities encompassing tilling, sowing, and harvesting. Our primary aim is to provide a meticulous and analytical exploration of the far-reaching implications of mechanization in peanut production. We emphasize the urgent need for cost-effective, socially responsible, and environmentally sustainable practices. Through a rigorous analysis of existing literature, we unveil the multifaceted impacts of mechanization on factors such as yield, quality, and losses in peanut cultivation. To ensure a comprehensive analysis, we primarily concentrated on peer-reviewed articles sourced from distinguished databases such as Scopus® and Web of Science™. Specifically, our review encompasses studies published between 1989 and 2022, covering a diverse collection of bibliographic resources. This range spans from the inception of the term "mechanizable peanut" to the most recent publications in these interdisciplinary databases. To refine our search, we developed research-specific search strings by combining relevant keywords, including "PEANUT", "TILLAGE", "SOWING", "DIGGING", "HARVESTING", and/or "MATURITY", along with appropriate Boolean operators. A team of reviewers meticulously evaluated the readability, consistency, and eligibility of papers by scrutinizing titles, highlights, abstracts, keywords, as well as materials and methods sections. We selectively included papers that aligned with our research scope, while excluding studies related to intercropping, rotation systems, fertilization, pests and diseases, and manual labor-related operations. Our findings highlight the potential for increased productivity and quality in agricultural systems that embrace mechanization, all while minimizing losses and their environmental impact. To optimize yield and quality while reducing losses, it is imperative for stakeholders to collaborate and endorse conservationist tillage practices, precision sowing techniques, and advanced harvesting methods. This meta-review serves as a call to action directed at the scientific community, policymakers, and producers, urging them to prioritize the integration of mechanization into peanut farming. This approach not only advances sustainable agricultural practices but also addresses the growing global demand for this essential crop. By examining advancements in conservationist tillage, precision sowing, and harvesting methodologies, we equip stakeholders with the knowledge required to enhance peanut cultivation. We particularly emphasize the importance of harnessing cutting-edge technologies, such as remote sensing for maturity prediction, to facilitate informed decision-making in the field. [Display omitted] • This paper provides a comprehensive review of the literature on tilling, sowing, and harvesting. • Explore advanced technologies like automation and sensors for enhanced efficiency and productivity. • Analyze the significant contributions of research and innovation to the advancement of precision agriculture. • Gain insights into the operation of agricultural machinery [ABSTRACT FROM AUTHOR]

The agri-food and rural development sector in Europe is undergoing digitalization, and therefore becoming increasingly data-dependent. However, limited experience in implementing Findable, Accessible, Interoperable, and Reusable (FAIR) data principles has restricted knowledge sharing and reusability. This study aims to investigate the existing FAIR Data Management practices in the agri-food and rural development sector by assessing the FAIRness of project outputs from recent H2020-funded Multi-Actor Projects (MAPs). We conducted a FAIRness assessment of project outputs using both semi-automatic and manual framework, and we also carried out a comprehensive review of the data sharing practices among selected MAPs. Of the investigated MAPs, <10% have achieved FAIR compliance and applied FAIR data management practices. The measured FAIRness of project products, including journal articles, conference papers, and books, is higher than that of other product types such as videos, audios, and presentations. The study highlights the critical need for standardizing the adoption of FAIR data principles and data management practices across both the agri-food and rural development sector but also across bureaucratic data management throughout Europe more widely. Such a change would facilitate broader utilization and re-use of MAPs outputs and results, enhancing decision-making and agri-food and rural development practices. [Display omitted] • The agri-food and rural development sector in Europe is becoming increasingly data-dependent. • The Open Science and the FAIR Data principles enhance decision-making by promoting FAIRness of agri-food research outputs. • The research community in the agri-food and rural development sector has limited experience in applying these principles. • Assessed FAIRness of project outputs in EU-funded agri-food and rural development MAPs. [ABSTRACT FROM AUTHOR]

Despite accelerated developments in agricultural innovations, there has been a limited progress in their adoption by smallholder farmers, especially in the Global South. Innovations are not adequately reaching farmers, and new technologies are often unsuitable for farmers, or the overall innovation landscape is discouraging the deployment of innovations at scale. The methodological approaches to designing, testing and scaling of innovations play an important role. Involving farmers in the innovation process from the start increases the likelihood of them being more relevant and having a higher chance of adoption. Whereas there is much literature on participatory approaches, there is a limited guidance on choosing an appropriate level of farmer participation across the innovation process. This study aims to develop practical guidance for researchers, development agents and project managers on how to choose an appropriate methodological approach with a suitable level of farmer participation for designing, testing and scaling of agricultural innovations, according to levels of innovation readiness and innovation use. Relevant scientific literature on participatory design, research and scaling of agricultural innovations was reviewed, followed by compiling lessons learned from selected vegetable innovation projects implemented by the World Vegetable Center (WorldVeg) in Asia and Africa. A list of methodological approaches for participatory design, testing and scaling of innovations was generated, with various levels of farmer participation. A simple guiding framework was then developed for choosing an appropriate approach with a suitable level of farmer participation, according to the readiness and use of the innovations. Examples of selected approaches and application of the proposed guidance are given in project case studies. Based on the literature review and experience at WorldVeg, we believe it is critical to choose the right level of participation based on how ready are the innovations for application in the local setting. This dimension of how ready the innovations are has been largely overlooked in earlier frameworks on farmer participation. The developed guidance generally recommends increasing participation with higher levels of technology readiness and use. In addition, farmer consultation in early research design should also be strengthened, to co-design locally relevant innovations. The overall innovation systems' landscape must be considered when researching and scaling new innovations. The guidance, experiences and lessons provided in this paper will be useful to various scholars, researchers, development agents, funders and project managers who aim to design, test and scale agricultural innovations in a more inclusive and responsible way. [Display omitted] • Despite rapid developments in agricultural innovations, there has been limited adoption by smallholder farmers. • There is limited guidance on choosing an appropriate level of farmer participation in the innovation processes. • A list of participatory approaches for design, testing and scaling of innovations was created. • A guidance for choosing a suitable level of participation based on 'innovation readiness and use' was developed. [ABSTRACT FROM AUTHOR]

Crop variety adoption is considered as one of the main strategies for farmers to enhance productivity under stress brought by the climate change, with less environmental impacts. It holds an upstream position in the whole life cycle of agricultural clean production, compared to the field management and agricultural non-point source pollution treatment. This paper aims to (i) assess the impact of new maize variety adoption on maize yield and fertilizer input; (ii) analyze the main determinants of farmers' new maize variety choice in the context of climate change, and (iii) identify the heterogeneity of small-scale and large-scale farmers in maize variety choice and its impacts. Based on plot-level data collected from three major grain producing provinces in China, we employ a treatment effects model to correct the potential bias resulting from the farmers' self-selection and estimate the direct impacts of farmers' new maize variety adoption and the main determinants of such adoption. The empirical results indicate that the adoption of new maize varieties significantly increases maize yield by 9.4% but fertilizer use by 16.6 kg/mu (1 mu = 1/15 ha) as well, and this trade-off between economic impacts and environmental impacts is more pronounced among small-scale farmers. However, in the face of natural disasters, adopting new maize varieties results in a significant reduction in yield losses by 1.4% and a decrease in fertilizer input for natural disaster management by 2.45 kg/mu, as compared to the adoption of old varieties. What's more, our findings suggest that a higher frequency of pest and disease disasters significantly increases the probability of new maize variety adoption by farmers, while a higher frequency of climate disasters significantly decreases the probability. The estimated impacts of new maize varieties point out the need of synergistically optimized resistance and nutrient use efficiency for crop breeding, which have important policy implications for sustainable food and agriculture. Also, the significant factor analysis contributes to the proposal of feasible ways to promote the farmers' adoption behavior. [Display omitted] • Crop variety adoption is considered as one of the main strategies for farmers to address climate change issues. • Impact of new maize variety adoption on yield and fertilizer input, and the determinants of such adoption are examined. • New maize varieties increase yield but fertilizer use as well. This trade-off is more pronounced among small-scale farmers. • Different natural disaster experiences have varying impacts on the probability of adopting new maize varieties. • Crop breeding needs to optimize resistance and nutrient use efficiency synergistically. [ABSTRACT FROM AUTHOR]

Institutional change strategies that prioritize transparency, fairness, and respect will advance inclusive agri-food systems innovation. This short communication posits that institutional change strategies are a linchpin, necessary for International Agricultural Research Centers to implement models promoting inclusive agri-food systems innovation. Models promoting inclusive innovation are pivotal for generating and advancing research for development actions tailored to specific contexts. Context-specific approaches demonstrate superior adaptability compared to universal actions. They contribute to the effectiveness of agriculture research for development, particularly in times of increasing volatility and inadequate progress toward global sustainability and equity goals. Targeted institutional change strategies foster three sister strategies that bolster inclusive innovation: navigating trade-offs; enhancing partnerships; and diversifying measurement. The paper stems from consultations within the Independent Science for Development Council (ISDC) and ISDC's related advising to CGIAR. Institutional change is the linchpin among four strategies identified by ISDC for driving inclusive agri-food systems innovation in Agriculture Research for Development. [Display omitted] • Context specific actions are needed to address gaps in progress toward global sustainability and equity goals. • Inclusive agri-food system innovation models generate and promote context-specific actions. • Strategies exist to advance inclusive agri-food system innovation models in agricultural research for development (AR4D). • Institutional change strategies are a cornerstone for inclusive and effective AR4D. [ABSTRACT FROM AUTHOR]

The dairy sector has been poised for fierce competitiveness due to the commercialization of the dairy sector and the presence of a global market size of 893 billion dollars. It has received a new dimension with the challenge of environmental sustainability. Considering all these challenges, the present research paper studies the major growth factors and the sustainable approach to dairy development in an emerging economy like India. Fifteen major dairy-developed states of India have contributed 90% of the milk production of the country examined from 2001 to 2019. The significance of key growth factors for dairy farming and the dairy value chain is assessed. The opportunity for sustainable dairying is examined. The authors have found that the growth factors of dairy farming and dairy value chain are significant by 1% through the fixed effect (FE) model and random effect (RE) model respectively. Indian dairy farming practices and their approach create optimism about environmental sustainability. The present study has observed a series of operational activities that are conducive to the triple bottom approach. With that, this study has mentioned strategies that can steer growth momentum and support sustainable Indian dairy development in the coming times. [Display omitted] • Three key areas, namely, dairy farming, dairy value chain and environmentally friendly practices for sustainable dairy development. • Two main types of variables (internal and external growth factors) are involved in the development of the dairy farming. • Strategies are formulated to improve milk productivity. • In line with the triple bottom line approach, a series of operational activities are suggested. [ABSTRACT FROM AUTHOR]

The increasing adoption of green agricultural development and recycling of agricultural resources policies in China in the recent past has led to the partial replacement of synthetic fertilizers with organic alternatives for fruit production. However, the nature, extent, and impact of this sustainable nitrogen (N) nutrition transition, i.e., combined synthetic and organic N fertilizer (combined N) use on the yield of fruit crops, fruit quality and soil quality remain unclear at the national level. The purpose of this study was to determine the effect of the combined application of organic and synthetic fertilizers on fruit-production systems in China and to gain more insight into the sustainable N management of fruit crops. A meta-analysis of peer-reviewed papers on this topic published from 2000 to 2022 was conducted. We conducted a meta-analysis of 84 studies with 399 comparisons in Chinese fields to examine the effects of combined N application on fruit yield, fruit quality and soil quality, and to identify the key factors in conjunction with random forest plots. The overall fruit yield, single-fruit weight, fruit soluble solids content, vitamin C content, and fruit solidity-acid ratio increased by 9.8%, 6.4%, 4.9%, 12.8%, and 14.8%, respectively, and the fruit titratable acid content decreased by 8.6% with combined N application. Additionally, compared with those that received synthetic N fertilizer only, the soil organic matter (SOM) content in the orchards fertilized with combined N increased by 20.0% and the soil pH increased by 1.1%. More specifically, when the organic N substitution rate (SR) was ≤70%, the fruit yield increased by ∼12%. The increases in soil pH, available N and available phosphorus (P) following combined N application were more pronounced in southern China than in northern China. Random forest analysis further revealed that the N application rate and SOM content were the two variables most related to fruit yield, fruit quality and soil quality, and geographic location was the most important variable explaining soil pH. These findings expand our understanding of sustainable N management and agricultural resource use and policy recommendations for sustainable orchard N management and high-quality fruit production in China and other countries with similar fruit production conditions. [Display omitted] • Combined application of organic fertilizer and synthetic fertilizer increased fruit yield, fruit quality and soil quality. • When the organic N substitution rate was ≤70%, the fruit yield increased by ∼12%. • The use of organic fertilizers on farms in southern China increased the soil pH by 3.1%, with no effect in northern China. • N application rate and SOM content were the two variables most related to fruit yield, fruit quality and soil quality. [ABSTRACT FROM AUTHOR]

The agronomy–livestock–forestry nexus is critical as it influences ecosystem services, food production, and land use. Asia, a diverse and technologically dynamic region, faces disparities in agronomy tech adoption. Studies emphasize agronomy and forestry's pivotal role in determining livelihoods in the region. Challenges like rising food demand, land scarcity, climate change, and biodiversity loss require innovative solutions. Digital technologies offer enhanced productivity, sustainability, and resource management opportunities, defining the era of smart agriculture and forestry. This work presents a Systematic Literature Review (SLR), which examines the potential for using digital technology in agronomy and forestry across Asian countries and evaluates evidence of the potential benefits for practitioners and the environment. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method guided the choice of relevant publications. Of the 375 papers located in Scopus and Web of Science (WoS) databases, only 24 were considered relevant to the research questions. In the agronomy and forestry sectors in Asia, the adoption of digital technologies has the potential to contribute toward the promotion of biodiversity, the preservation of ecosystem services, improved labor efficiency, risk reduction, and the promotion of climate change resilience. Geospatial tools, modeling tools, decision support systems, Unmanned Aircraft Systems (UAS), and the Internet of Things (IoT) have emerged as prominent technologies driving these positive outcomes. The study lists evidence from various articles supporting these benefits, although most demonstrate indirect causation. This underscores the need for more direct experiments to establish the broader contribution of digital technologies as well as provide evidence showing farmer uptake and the potential negative impacts of implementation. The findings from this study offer a comprehensive view of the potential use of digital technologies for agronomy and forestry in Asia, as well as evidence of their potential benefits. It gives stakeholders valuable information on digital technologies and provides a springboard for future studies regarding the application of digital technology in agronomy, animal husbandry, and forestry. [Display omitted] • Digital solutions can mitigate the impact of uneven tech adoption on livelihoods in Asia's agronomy and forestry sectors. • Presents SLR provide the evidence of benefits for practitioners and environment. • Key technologies include geospatial tools, modeling tools, decision support systems, UAS, IoT. • Digital tech enhances Asia's agronomy/forestry by improving biodiversity, labor and risk management. • Provides information for stakeholders and highlighting research potential. [ABSTRACT FROM AUTHOR]

Crop breeding teams should ideally work a-priori to rank research priorities and varietal product concepts to make decisions considering social, environmental, production and market conditions that an improved crop variety will be entering upon release. Frequently, this research prioritization is guided by a cost-benefit optimization framing, which leaves the potential derived by alternative approaches, such as inclusive innovation, unexplored. In this study, we apply PEEP (Participatory Ex-ante framework for Plant breeding), a new methodological framework for ex-ante participatory priority setting for plant breeding programs. This framework explores how, given a set of hypothetical crop varieties (varietal product concepts) it is possible to assess which will have the highest potential to deliver research impacts. PEEP is designed to support crop breeders to understand the impact potential of a new varietal product concept, and strategically make decisions based on this. PEEP ranks varietal product concepts across five impact domains: breeding, processing, marketing, climate, and cross-cutting themes. This paper reports on how an interdisciplinary breeding team from the Institute of Environment and Agricultural Research (INERA) in Burkina Faso working on cowpea (Vigna unguiculata [L.] Walp) applied PEEP and discusses the results from this application. We chose cowpea as a crop focus, based on prior research that showed that gender and social differences shape roles, responsibilities, and benefits from its cultivation in Burkina Faso. We engaged 650 stakeholders using tools and methods (choice experiment, surveys and focus group discussions) tailored to collect information according to their spheres of knowledge. We present a reflection on the application process, results, and feedback from the INERA cowpea breeding team on these. We show that breeders and farmers ranked the potential impact of the varietal product concepts differently. We also show that differences in evaluation of impact from varietal product concepts vary based on market orientation, agroecological zones, farming systems and gender. We demonstrate that PEEP contributes to inclusive agricultural innovation as a feasible method to consider the knowledge and preferences of diverse stakeholders. We show that it is informative to include diverse groups when conceptualizing new varieties before investing in varietal development. Making such research prioritization decisions early on enables breeding programs to avoid "missing the mark" on assumed impacts on smallholder women and men farmers and increases their visibility and voice in these upstream decisions. [Display omitted] • When teams work a priori to rank research priorities in public crop breeding, this is often a cost-benefit optimization. • We propose PEEP, a participatory framework, intentional about gender, which seeks to encompass heterogeneous benefits. • The Institute of Environment and Agricultural Research in Burkina Faso applied PEEP on cowpea through a reflective process. • Evaluation asymmetries emerge between breeders and farmers, with social variables shaping perceived potential of impact. • We call for more research prioritization a priori that increases the visibility and voice of smallholder farmers. [ABSTRACT FROM AUTHOR]

Climate services are integral to climate change adaptation in agriculture. Inclusive innovation prioritises inclusion of marginalised actors, grassroots knowledge and accessibility of use. Yet many innovation processes are multi-stakeholder, complex and may not prioritise inclusion from the outset. In these circumstances, researchers and designers may seek to influence existing projects towards greater inclusivity. This paper explores efforts to increase the inclusivity of existing innovation processes, focusing on the design of My Climate View, an online Australian climate services platform for agriculture. Findings relate to contemporary issues in inclusive innovation literature, including: (1) Who should be included in innovation processes (within given resourcing)?; (2) How requirements for inclusion or exclusion can emerge through the design and use of a technology; and (3) The influence of program level structures upon inclusivity and design. Our approach is informed by Responsible Innovation and Inclusive Innovation theory and enacted through user-centred design. Findings are synthesised from analysis of 60 semi-structured interviews with farmers, advisors and extension officers. We additionally draw on observations as researchers in the multi-actor My Climate View development team, where extensive engagement separate to research activities has included agricultural and Indigenous stakeholders. Engaging with a broad sample of potential target users enabled diverse and sometimes unexpected use cases. Customisation of interface parameters allowed the incorporation of tacit knowledge and positively affected the inclusivity of My Climate View, extending the value of the platform to growers of niche commodities outside of the original scope. Inclusivity in innovation can be impacted by diverse factors, including design team decisions, structural factors, budget and external factors. Requirements for inclusion and exclusion can emerge and evolve throughout a design process, supporting Responsible Innovation's framing of inclusivity alongside principles of reflexivity and responsiveness. The findings enrich the evidence base for climate services and broader agricultural technology development processes by providing insights for those seeking greater impact by practicing inclusivity in innovation. We provide four implications for increasing inclusivity in the design of climate services: (1) Maximising customisation and avoiding prescriptive interpretation enables greater flexibility of use; (2) Sampling broadly within target users to uncover unanticipated values and opportunities for inclusion and exclusion; (3) It is important to embed efforts towards inclusion alongside processes of reflexivity; and (4) Responsible and inclusive approaches to climate services design are valuable even if brought in once development has commenced. [Display omitted] • Explores the challenges of steering a largescale climate services innovation project towards greater inclusivity. • Maximising customisation options for users and avoiding prescriptive interpretation enables greater flexibility of use. • Responsible and inclusive approaches to climate services design are valuable even if adopted once development has commenced. [ABSTRACT FROM AUTHOR]

As the concept of social-ecological resilience gains increasing policy attention, there is growing demand for approaches that operationalise it. Amongst these demands is the need to empirically assess absorptive, adaptive and transformative capacities that underpin resilience to better understand the ways in which social-ecological systems can navigate change and uncertainty. We explore the application of a capitals approach for assessing resilience capacities, using an example of deciduous fruit farming operations in the Western Cape region of South Africa. Semi-structured interviews were conducted, thematically coded, and analysed using causal loop diagrams and co-occurrence analyses to identify changes experienced by farming operations, their responses to these changes, and the effects of both on capital resources. We then apply the criteria developed for the classification of resilience capacities. A variety of changes affect farming operations at multiple points across the value chain, and have effects across capital resources. Most significant to farmers are changes which impact the amount of water available and the cashflow they require to sustain their operations. The most common responses employed by farmers consisted of activities which either increase the availability of an affected resource, or decrease its demand in order to maintain the same functions (adaptive capacity). In fewer cases, farming operations were able to absorb the depletion of their capital resources due to pre-emptive management (absorptive capacity). In similarly few cases, the potential or realised resource deficiencies caused by changes were either corrected or decoupled from the farming operation through structural reorganisation towards a different or additional function and types of output being delivered (transformative capacity). Evidence of changes being anticipated prior to their onset were also identified, leading to responses which are used in conjunction with the preceding three capacities. Our findings suggest that the criteria we developed for classifying resilience capacities offer a useful means of operationalising resilience. In particular, the approach we pilot in this paper enables the application of a systems perspective to identify interactions between changes and responses, which broadens the options for identifying management strategies and interventions. The approach we propose can be used to identify key leverage points to strengthen the capacities of vulnerable farmers. Further work is required to integrate consideration of cross-scale effects of farm-scale resilience strategies on the broader social-ecological system. [Display omitted] • Empirical examples of studies which operationalise social-ecological resilience are in high demand. • We assess resilience identifying the capital effects of change and responses in farming operations. • The interaction between changes and responses are used to infer capacities of resilience. • Most response strategies reveal adaptive capacity, with other capacities utilised to a lesser extent. • The criteria employs a systemic outlook which broadens considerations for responses and management strategies. [ABSTRACT FROM AUTHOR]

Peste des petits ruminants (PPR) remains a persistent challenge for small ruminant production in Senegal, leading to economic losses for livestock keepers. Gender dynamics and socio-cultural factors influence production decisions at the household level. Despite its relevance, economic impact models of PPR have not incorporated gender dynamics. This paper seeks to redress this gap by disaggregating the farm-level impacts of PPR based on the gender of the household head and implementers of routine and management activities in small ruminant production at the household level. To quantify the gendered economic impacts of PPR, a system dynamics model was developed with four modules: integrated production-epidemiology, economics and profitability, disease control, and marketing. The model was parameterised based on survey data from 747 households in 49 agro-pastoral and pastoral villages in Senegal and augmented by secondary data. The model was validated (structurally and behaviourally) and simulated for 30 years with a weekly timestep. A set of disease scenarios were run for ten different farm household typologies based on the gender of the household head and household members who perform production management activities (like purchasing, selling, gifting, and vaccinating animals) and routine activities (like feeding, cleaning pens, and caring for sick animals). Model results estimated annual reductions in farm profitability of 51% to 61% with a hypothetical 25% incidence rate of PPR, corresponding to an average of $1051 to $1246 USD lost per household per year. In the two most impacted household typologies, management activities were performed by either men only or women only, and in the three least impacted household typologies, management activities were shared by men and women. The least impacted household typology with the highest gross margins without PPR ($2429 USD per year) and the lowest percentage of loss (51%) was female-headed households with men and women doing management tasks and primarily women or girls doing routine activities. Households with both women and men engaged in management activities were less impacted by PPR, highlighting the crucial role of women's agency in management decisions taken in small ruminant production at the farm level. The findings further provide the empirical grounding for considering gender dynamics in interventions to control PPR and other livestock diseases in small ruminant production systems. [Display omitted] • PPR leads to economic losses for affected livestock keeping households. • The distribution of work and decision-making amongst household members is crucial for PPR eradication. • Interventions may benefit from incorporating gender transformative approaches in communities. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has posed unprecedented challenges to the livelihoods of rural households, especially in developing countries. The reverse migrated workers faced the most economic crisis. They have created additional pressure on local resources and opportunities to maintain their livelihood. In this connection the agriculture-livestock-forestry nexus plays a significant role by providing them livelihood opportunities. This paper attempts to understand the resource use patterns during the post-COVID period, and analyse the impact of agriculture-livestock-forest nexus on household income diversification and poverty reduction. The present study uses a novel approach to fulfil the objectives of the study. A well-designed survey was conducted to collect the necessary primary data from sixteen sample villages of West Bengal, India. The scientific design of the survey and data collection procedure is maintained. Moreover, geospatial techniques are used to examine the land use and land cover patterns of the study areas. The econometric models are estimated to inspect the role of agriculture-livestock-forest nexus and household characteristics on household level income diversification and the reduction of poverty. Our analysis reveals that a significant number of the sample households depend on agriculture, livestock and forestry to maintain their livelihoods. The reverse migration of workforce has created additional pressure on these activities. This is mainly due to non-availability of other non-farm employment opportunities locally. The analysis of econometric models shows that while engagement in different activities of the primary sector makes income diversified, it is more so when the households possess necessary technologies, skills and financial supports. All of these are crucial given that income diversification helps households to overcome of poverty. The intricate web of the agriculture-livestock-forestry nexus serves as a critical source of livelihood for a substantial number of households, yet the challenges and opportunities it presents necessitate targeted policy interventions. Enabling technology transfer, fostering skill development, and extending financial support emerge as key measures to diversify rural livelihoods, thereby fostering sustainable rural development and poverty reduction in the post-pandemic landscape. The integration of socioeconomic data and geospatial satellite information offers an invaluable basis for formulating effective policy strategies in this evolving era. [Display omitted] • Examines the agriculture-livestock-forest nexus and its impact on income diversification during the post-COVID period. • Analysing resource use and livelihoods with primary data, geospatial and econometric methods. • Technology transfer, skill development, and financial aid boost rural income diversification. • Socio-economic and satellite data integration aids in crafting impactful policies. [ABSTRACT FROM AUTHOR]

This research paper details the response to a central challenge of the digitalization of agricultural systems which stems from information overload and the broad heterogeneity of agricultural technologies and the classification thereof. It is the result of four years of effort on the Horizon 2020 SmartAgriHubs (SAH) project, whose purpose was the development and connection of Digital Innovation Hubs (DIHs) and Competence Centres (CCs) related to the agricultural sector in Europe. CCs form the backbone of DIHs and provide the digital technological infrastructure by offering advanced technical expertise, access to the latest knowledge and information on digital technologies, and other technological and scientific infrastructure. However, the lack of a general framework for technology classification in agriculture needed to be remedied, not only to specifically advance the general aim of SmartAgriHubs and DIHs to "connect the dots", but in general, so as to facilitate a broad range of actors involved in digitalisation of agriculture. This paper sets out the conceptual framework for the classification of technologies in the agricultural sector, and the development of the "Agricultural Technology Navigator" (ATN) as an iterative tool for gathering and searching for information to support such classification, so that actors involved in the digitalization of agriculture may more easily find and communicate technologies related to digital solutions. A multi-actor, multi-disciplinary expert group was formed to identify technologies and define keywords. A literature, patent, foresights studies, and grey literature review was also carried out. Text mining techniques, using the JCR "Tool for Innovation Monitoring", were used based on keywords selected by experts for documents analysis (+170,000,000 publications; + 22,000,000 patent applications; + 77,000 projects). Gaps in previous studies were identified to form the basis upon which to create a new classification system. Taking into account the fact that technologies are continually developing and evolving, an iterative approach was also necessary. To this end, a new tool was developed, compatible with agricultural supply chains, to gather "bottom up" information from CCs (and other actors) on innovative digital technologies and solutions. The main results of all the publications, patents and projects summarized herein, provide a scientifically sound foundation for a much-needed classification of digital technologies used in agriculture. This paper analyses different dimensions or categories in which the diverse technologies can be classified and selects the most appropriate classification to describe the technologies which could be used and managed by CCs and other agricultural actors, in offering and searching for systems and competences which provide digital solutions. Given the importance of DIHs and CCs, and more recently European DIHs (EDIHs), in the European agricultural policy agenda and the development of the sustainable digitalization of agriculture, it is necessary to have a mechanism to appropriately classify digital technologies relevant for agriculture so that providers and users can "match-make" and easily offer, find, and decide on relevant digital tools and solutions. CCs are not the only entities that benefit from such classification. A diverse range of actors involved in the sustainable digitalisation of agriculture, such as technology companies, farm advisors, machinery and other input suppliers who develop and deal in novel and different technology innovations, can benefit from being able to describe appropriately their technologies as part of agricultural systems and solutions. [Display omitted] • A conceptual framework for the classification of technologies in Agriculture is proposed. • The classification is based on information provided by Competence Centres and heterogeneous information sources. • Technologies are treated in the framework of systems and competences. • The ATN has been developed as a new tool for gathering information about systems and competences in agriculture. • Results come from the Horizon 2020 SmartAgriHubs (SAH) project dealing with Digital Innovation Hubs in agriculture. [ABSTRACT FROM AUTHOR]

Multi-stakeholder partnerships (MSPs), which involve the collective action of various institutions such as governments, private companies, NGOs, and donors, have become a popular approach for developing agricultural value chains to improve the sustainability of food systems. However, the growing body of literature presents a range of terms, concepts, and definitions related to MSPs, potentially causing confusion and obscuring the distinctions between different types of partnerships. While the potential benefits of MSPs have been extensively explored in the literature, there remains a need to investigate their realized impacts across regions to enable a more robust synthesis. The objective of this study was to systematically review the literature on MSPs in agricultural development, specifically examining the various concepts employed and assessing their realized impacts on farmers, focusing on developing countries. We systematically reviewed the literature on MSPs and identified 147 studies dealing with a different type of MSPs and 79 studies reporting the impact. We found that twelve terms have been used to describe MSPs. However, a single MSP initiative may be referred to by different names in various documents, indicating a lack of consensus on the defining characteristics of such partnerships. Positive impacts were reported on farmers' economic, social, and technological aspects, as well as environmental adaptation practices to address climate change. Nevertheless, the reported impacts tend to focus primarily on economic, technological, and social dimensions while giving less attention to environmental aspects. This paper presents, firstly, a comprehensive overview of the diverse practices of MSPs in developing countries. Secondly, it analyses the contributions made by MSPs across various commodities and regions in the development of agricultural value chains. [Display omitted] • Multi-stakeholder partnerships (MSPs) have become a popular approach for developing agricultural value chains. • This study examines the various concepts employed and assesses their realized impacts on farmers. • We found that a lack of consensus on the defining characteristics of such partnerships and positive impacts were reported. • This paper presents a comprehensive overview of the diverse practices of MSPs and analyses their contributions. [ABSTRACT FROM AUTHOR]

To design policies for sustainable irrigation planning, public decision-makers need knowledge about the potential environmental impacts of irrigated farming systems. In a previous paper, we proposed to couple Life Cycle Assessment (LCA) and Agrarian System Diagnosis (ASD) to build the Life Cycle Inventory (LCI) of a farming region in a context of data scarcity and farming system diversity. This method was applied to the semi-arid irrigated plain of Kairouan (Tunisia). The aim of the present paper is to demonstrate how this new ASD-based LCA method is used to conduct the impact assessment of this farming region, under current conditions and for two prospective scenarios. Two functional units (FU) were used: the area and the gross product generated. Life Cycle Impact Assessment (LCIA) was conducted based on a multilevel process, by converting LCI data collected for each cropping/ livestock system into LCIA outputs, which were then consolidated at the farming system (FS) and at the regional scales. Two scenarios, based on observed trends, were assessed: the "agrarian" scenario (driven by a shift from family to corporate farming) and the "hydrological" scenario (driven by a water table drawdown). Environmental hotspots were identified at each scale: the most impactful FS was the "mixed family farming" (intermediate access to water), because of its large share of land at the regional scale (45%). In this FS, the most harmful cropping system was input-intensive olive groves intercropped with a melon-pepper/ cereal/ tomato rotation, in which most impacts (35%) were attributable to the input-intensive "melon" crop, and more particularly to "manufacture and transport of fertilizer", including farmyard manure. Globally, the most harmful processes were water and fertilizer use (fertilizer manufacturing and transportation). This downscaling is very useful to inform public policies on the most harmful productions. The impacts in human, ecosystem and resource endpoints increased by 11%, 2%, 9% and 23%, 27% and 40%, for the agrarian and hydrological scenarios respectively. These results show that observed trends in farming practices evolution (more irrigation) should be stopped. Our method has three main strengths: i) inter-farms synergies are accounted for in the LCIA, which is essential to promote circularity; ii) thanks to the innovative and multilevel approach, the origins of environmental hotspots can be investigated and later mitigated by future policies; iii) ASD helps to design sustainable scenarios by identifying current trends and regional thresholds (resources, pollution and workforce) that cannot be overcome. [Display omitted] • Multilevel Life Cycle Impact Assessment (LCIA) was conducted in a semi-arid irrigated plain (Tunisia). • Agrarian System Diagnosis and Life Cycle Assessment methods were coupled. • Environmental hotspots are modeled from the plot /herd, up to the regional scale. • Main impacts are caused by water depletion, fertilizer manufacturing and manure transportation. • Environmental impacts may increase by 40% in trend evolution scenarios (watertable depletion and less familial farming). [ABSTRACT FROM AUTHOR]

Recently, smart agriculture has become an essential part of modern agriculture approaches from tillage, via plant seeding and grow support to their collection. With modern technologies, farmers can use substances like pesticides, herbicides, or fertilizers at precise dosages or to identify places on a field with specific production rates. The main objective of this study is to introduce a novel and a unique aerial image dataset of various fields acquired by UAV containing crops/weeds in the early phenophases captured in two different resolutions (2 mm and 7 mm per pixel). Secondly, the best super-resolution technique for high-resolution images, substitution with lower resolution is explored. For data acquisition, we employed DJI Matrice 600 equipped with a full-frame Sony Alpha A7R IV285 image sensor. Data were captured at flight heights of 26 and 95 m from 4 different fields in Central Europe. In addition, we proposed a methodology focused on the selection of an appropriate super-resolution method to enhance low-resolution aerial images to obtain better accuracy of crop/weed detection. As a baseline crop/weed detector for super-resolution effect evaluation, YOLOv5 architecture was used. Next, we explored the performance of several super-resolution models (U-Net++, ESRGAN, SwinIR), and fine-tuned the best-performed one. We present the new dataset named SPAGRI-AI: a novel unique dataset of aerial images for super-resolution experiments in smart precision agriculture. The dataset contains 27,638 aerial images (1024 × 1024 px) and additionally, it contains a subset of 2014 labeled images with 45,548 bounding boxes of 12 classes. The main purpose of the SPAGRI-AI is to provide the scientific community with real-world data to test new methods for super-resolution (SR) and crop/weed detection. During the evaluation of selected super-resolution models, the YOLOv5 model trained on high-resolution images resulted in corn mAP@0.5 of 94.48%. The YOLOv5 model trained on low-resolution images resulted in corn mAP@0.5 of only 51.43%. Nevertheless, if the low-resolution images were pre-processed using the SwinIR super-resolution method, corn mAP@0.5 of 62.36% was achieved. To the best of our knowledge, it is one of the largest datasets available to the paper's publication date. Overall, the SPAGRI-AI dataset and the findings from our experiments contribute to the advancement of super-resolution techniques and crop/weed detection methods in the field of smart agriculture. By utilizing real-world data and optimizing image enhancement approaches, we paved the way for further developments in precision farming practices and applying emerging technologies in agriculture. [Display omitted] • Dataset consists of 27,638 aerial images captured at two different flight heights (26 and 95 m). • Dataset contains a subset of 2014 labeled images with 45,548 bounding boxes across 12 distinct classes. • Baseline model trained on higher resolution data (127 PPI) achieved up to 95% of mAP@0.5 for corn detection. • Model employing super-resolution method on lower resolution data (36 PPI) improved detection performance by 11%. [ABSTRACT FROM AUTHOR]

To improve agricultural sustainability, alternative cultivation methods and assessment tools need to be developed. Integrating service crops (SC) can potentially increase cropping system multifunctionality and mitigate negative climate and environmental impacts of agriculture. (1) Calibrate oats, winter wheat and red clover SC, grown as sole crops and intercrops, in the cropping system model APSIM NG for northern Europe climate conditions. (2) Use the calibrated crop modules to assess ecosystem processes from an intercropping system. (3) Discuss the role of mechanistic crop models in assessing ecosystem services and disservices from complex cropping systems. The crops were calibrated with data from an oats-winter wheat cropping sequence at two field sites. Thirty weather datasets were created from historical weather data to generate weather-dependent variability in crop performance and related processes. The assessment compared two scenarios, with or without an intercropped red clover SC sown in oats and terminated the following spring in winter wheat. Outputs representing processes related to important ecosystem services were extracted from the simulations. Calibration of the three crops resulted in satisfactory biomass levels at the end of the growing season. Including a SC reduced oat yield, but increased winter wheat yield in two-thirds of simulations. Model outputs showed that including a SC resulted in 33–79% more fresh soil organic carbon, depending on site, compared with no SC. Nitrogen (N) uptake by both crops was highest in the SC scenario. In oats, N losses did not differ between the two scenarios, while in winter wheat the SC scenario had approximately 50% lower N leaching losses and 30% higher gaseous N emissions. However, in the fallow period from winter wheat harvest through to spring, both types of N losses were elevated in the SC scenario. The SC scenario had only a minor effect on water dynamics, causing a small reduction in soil water content. In this paper we give an example of how APSIM NG can be used to assess ecosystem services from complex agricultural systems using a case study with intercropping of cereals and leguminous SCs. APSIM NG was useful in providing a holistic assessment, and we show that intercropping with a SC can improve cropping system performance and reduce negative impacts, but long-term strategic management of N is required to prevent increased losses. To further improve simulation of intercrops more accurate simulation of early growth is needed for all crops included. [Display omitted] • Crop modules for oats, winter wheat and red clover were calibrated to simulate intercrops in Sweden in APSIM NG. • Effects of intercropping cereals (oats-winter wheat) and a legume service crop on cereals and soil processes were assessed. • Calibration resulted in satisfactory end-biomass, but poor simulation of biomass during leaf development and tillering. • Intercropping with a service crop had positive effects from an ecosystem service perspective. • Drawbacks were elevated N losses in winter wheat and fallow, indicating a need for long-term N management. [ABSTRACT FROM AUTHOR]

Accurately detecting and counting fruits is essential for orchard yield estimation and smart management. However, two drawbacks are inherent in the current citrus counting algorithms: First, the robustness needs to be improved, particularly illumination changes and dense occlusion. Secondly, the actual operating efficiency of the system is low. To tackle the above issues, this paper proposed a robust and efficient fruit-counting pipeline based on Unmanned Aerial Vehicle (UAV). First, to obtain UAV video streaming data online, a live broadcast platform and a flight control Application called FlyCounter were developed. Secondly, the Illumination-Adaptive-Transformer network is used to enhance the low-illumination citrus image in real-time. Then, for the specific challenging scenario, a novel model named Fruit-YOLO is designed to accurately detect citrus in data streams. Finally, the DeepSORT is adopted to track and count fruits in video sequences. The results indicate that for detection performance, the P, R and mAP of the Fruit-YOLO of input enhanced image are 0.898, 0.854 and 0.929 respectively, which are 1.7%, 4.6% and 3.5% higher than the original image respectively. Regarding counting performance, for the daytime and evening scenes, the ID switch, MOTA and Error Mean of the pipeline are 63.7, 0.86 and 9.81% respectively. The MAPE of the offline and online operations of the pipeline are 4.36% and 12.66% respectively. The time and resources consumed by the system under parallel operations with different numbers of UAVs were analyzed. In this study, an online counting pipeline based on UAVs that can work in low-light scenarios is implemented for the first time. The system has good performance in both daytime and nighttime scenarios, enabling efficient counting in orchards and extending operating time. OBJECTIVE: To implement a robust and efficient fruit counting system for large-scale unstructured orchards. METHODS: First, a new UAV platform and a flight control application are proposed to transmit UAV data streams; then, a detection network named Fruit-YOLO is designed, and combined with the IAT network and DeepSORT algorithm to estimate the overall number of fruits in the orchard. RESULTS: The system achieved a mAP of 0.929 and a MAPE of 12.66%. CONCLUSION: The system has good performance in both daytime and nighttime scenarios, enabling efficient counting in orchards and extending working time. [Display omitted] • An End-to-end and efficient counting system for orchards using UAVs. • A new UAV platform and flight application. • A novel detection model is proposed for UAV scenarios. • Introducing IAT network to improve the system's performance under low illumination. • Improve work efficiency by using multiple UAVs to operate simultaneously. [ABSTRACT FROM AUTHOR]

A key strategy in progressing towards the Sustainable Development Goals (SDGs) is scaling innovations to improve livelihoods of marginalized populations globally. Consequently, there has been a heightened emphasis on Agriculture Research for Development (AR4D) innovation teams' ability to swiftly identify innovations that can be scaled for broad impact. However, insufficient attention has been paid to the equitable distribution of benefits among different demographic groups, leading to documented unintended consequences affecting some of the most marginalized communities. To effectively contribute to the SDGs and avoid harm to specific groups, AR4D innovation teams must conscientiously consider various dimensions of diversity, including gender and other relevant factors. Our objective is to introduce GenderUp, a new conversational method for responsible scaling, oriented to making scaling initiatives more inclusive and anticipatory of socially differentiated trade-offs. This new AR4D scaling tool seeks to ensure that scaling efforts not only contribute effectively to the SDGs but also consider and mitigate unintended consequences for marginalized communities. This paper describes a new socially responsible scaling tool. Starting with a defined innovation, GenderUp helps: i) identify relevant diversity and intersectionality among intended innovation users; and ii) re-direct scaling strategies to better anticipate and mitigate unintended consequences for specific social groups. Through a series of five stages, a GenderUp facilitator guides teams through discussions, learning activities, and practical integration to develop a socially responsible scaling strategy. We provide details about each stage and reflect on the results of two early pilot experiences. There is a need for increased recognition of the distribution of innovation benefits based on dimensions of diversity. GenderUp is proposed as a new method to systematically address social differentiation in the scaling of AR4D innovation. GenderUp supported two different innovation and scaling teams to rethink their scaling strategies. Both innovation teams identified at-risk intersectional profiles and made adjustments to their scaling strategies to ensure these groups were not harmed by the innovation or scaling activities. These promising examples suggest that GenderUp provides a simple ex-ante analysis that can reduce unintended consequences more broadly within AR4D. Although gender and social equity are prioritized globally there are very few experts, tools, and systems in place to achieve these cross-cutting goals. While GenderUp only addresses innovation at the scaling stage, it is a step towards systemizing responding to social differentiation within AR4D to at least do no harm, and at best improve social outcomes. [Display omitted] • Research for Development actors increasingly rely on achieving Sustainable Development Golas through scaling innovation. • Gender and social equity are global priorities, but tools for addressing social differentiation in scaling are lacking. • Based on Responsible Research and Gender studies, GenderUp is a method for more equitable agricultural innovation scaling. • GenderUp guides innovation teams through five stages emphasizing conversations, learning, reflexivity, and integration. • GenderUp fosters a socially responsible scaling strategy, helping to advance gender-positive and socially equitable outcomes. [ABSTRACT FROM AUTHOR]

Optimizing greenhouse design is a complex challenge that involves the large combinational solution space and the interactions between design elements, outdoor climate, and crops. In addition, evaluating greenhouse performance requires consideration of economic and environmental dimensions, as well as different stakeholder priorities. This study aims to identify greenhouse designs that are efficient in terms of economic and environmental performance for both policy makers and investors for four locations in China. This paper made a novel combination of operational research methods with bio-economic modelling. Specifically, a bio-economic model was used to simulate the yield, energy use, and economic and environmental performance of different greenhouse designs. A genetic algorithm was used to explore the large solution space to reduce the computational effort. The overall performance of greenhouse design was evaluated using a directional distance function approach, which incorporated stakeholders' priorities for economic and environmental performance through the directional vector. The results identify several greenhouse designs that were found to be efficient in terms of economic and environmental performance for both investors and policymakers across various price scenarios. The most influential factors on operating income include the choice of lighting, structure, thermal screen, and CO 2 dosing rate. Among lighting options, LED lighting outperforms HPS lighting in terms of both economic and environmental performance. Specifically, incorporating LED lamps with an intensity of 200 μmol m−2 s−1 can increase annual operating income by 97.3 to 200.2 ¥ m−2, depending on the region. Conversely, low intensity lighting adversely impacts both economic and environmental performance. A synergistic relationship has been observed between lighting and CO 2 dosing. On the other hand, lighting is the primary contributor to greenhouse gas (GHG) emissions. Incorporating LED lighting with an intensity of 200 μmol m−2 s−1 can increase CO 2 equivalent emissions from 151.7 to 211.0 kg m−2. Incorporating thermal screens can effectively reduce GHG emissions. The study presents an optimization approach that can be applied to various complex agricultural systems with interactions between many factors and the presence of multiple stakeholders with conflict priorities. In addition, our study has practical implications for the greenhouse sector in China. The wide range of optimal solutions provides policy makers and investors with the flexibility to choose greenhouse designs that meet regional agricultural development goals and budget constraints. [Display omitted] • Greenhouse design needs to consider production, economic, and environmental interdependencies, andstakeholder preferences. • We used bio-economic modelling, genetic algorithm, and a directional distance function approach to evaluate greenhouse designs. • We identified several greenhouse designs with robust economic and environmental performances for four locations in China. • Lighting system, structure, thermal screen, and CO 2 dosing rate were among the most influential factors on operating income. • Lighting is the primary contributor to GHG emissions, while the use of thermal screens can effectively reduce GHG emissions. [ABSTRACT FROM AUTHOR]

Crop models are essential tools for assessing the impact of climate change on national or regional agricultural production. Starting from meteorology, soil and crop management, fertilization and irrigation practices, they predict the yield of specific crop varieties. For long term assessments, climate models are the source of primary information. To make climate model results usable in a specific time frame context, bias adjustment (BA) is required. In fact, climate models tend to deviate from day-to-day values of the physical parameters while conserving the climate variability signal. BA brings the climatic signal to the actual values observed in a specific location and period, and to be representative of a specific period in absolute terms. BA techniques come in different flavours. The broadest categorization is univariate and multivariate methods. Multivariate methods adjust the variables considering possible cross-correlations while univariate methods treat the variables one by one without accounting for possible dependence on one another. The hypothesis tested in this paper is that since crop models require as input climate variables that are in most of the cases cross-correlated, the multi-variate bias adjustment of the latter is likely to improve performance compared to univariate bias adjusted climate model results. To verify this hypothesis, 14 BA methods were applied to 9 variables from 8 climate models at 21 locations across Europe and Northern Africa for a period of 5 years. Twelve crop models, from the AgMIP Wheat community, were run using the climate model results. All crop models, except one, were restarted at every growing season. The crop models were also run using the AgMERRA re-analysis. The latter were used as reference to compare the results when using the other climate models treated with the various sets of bias-adjustment methods. The results show that multivariate BA treatment should be preferred to univariate ones. The error obtained by comparing crop simulation obtained with AgMERRA with those obtained with multivariate bias-adjusted climate prediction is systematically lower. The error reduction varies as a function of the variable, the location, the crop model, and the climate model though the tendency is for smaller errors when multivariate methods are used to treat the latter. The results are attributed to the nature of crop models and the fact that multivariate methods consider more adequately the correlation existing between the meteorological variables. The study shows the importance of considering the nature of a model and the selection of input data that best suited to the former. In this case the improvements produced when using multivariate data appears to be significant especially in the light of the variety of crop models used and the similar response obtained and it is therefore recommended. [Display omitted] • The impact of climate data multivariate bias-adjustment methods versus univariate on crop model results was estimated. • Crop model results improved when input data was treated using multivariate methods compared to univariate methods. • Multivariate methods maintain the variables correlation as required by crop models. • This result is attributed to the parameterized nature of the participating crop-model formulations. • Climate, bias adjustment and the crop modelling communities joined efforts in this unprecedented collaboration. [ABSTRACT FROM AUTHOR]

Climate change and extreme weather events undermine smallholder household food and income security in many rural areas of developing countries. Nature-based solutions and the use of climate resilient and nutrient dense Neglected and Underutilized Species into the local farming system have been claimed to sustainably help farmers adapt to climate change and to manage risk more effectively. This paper investigated the potential implementation of three options based on the collective cultivation and management of olive groves and wild forests for production purposes in a mountain region of Nepal. The analysis was aimed at assessing the potential benefits of such strategy in comparison with the status-quo. The study conducted an ex-ante simulation exercise for evaluating three options: 1) 1500 ha of new and restored wild forests; 2) 500 ha of olive groves; and 3) A combination of option 1 and 2. We calculated the financial, economic, social, and environmental costs and benefits of such options using agricultural and economic data collected from representative households identified from a sample of 31 small, 9 medium, and 3 large farm households. To conduct the economic, social and environmental analysis (ESE), we calculated the Social Return on Investment and we integrated it into the economic analysis. Then we compared the results to assess which was the most profitable option. The results of the analysis showed that all three proposed options are stable with an Internal Rate of Return always above 27% with values over 100%. The greatest increase in terms of NPV is observed when shifting from the financial to the ESE analysis in options 1, 2, and 3 (+397.5%, +132.0%, +187.2% respectively) as the benefits increase more than the costs. The results further suggested that the integrated option is the most profitable one also in terms of labour generation. Indeed, we observed a substantial increase in the Return to Labour indicator especially in the ESE analysis (from 1.9 in the financial analysis to 2.8 in the economic analysis to 6.0 in the ESE analysis). This study provides evidence of the potential that similar strategies based on the use of local resources and species, if properly designed, implemented and managed, may have to restore the ecosystem and enhance the livelihoods of local communities. The most innovative element of this study is the proposed ESE analysis, which takes into account the social and environmental dimension besides the economic one. [Display omitted] • Olive growing would be a nature-based solution to sustainably diversify the local production system. • Assessing the benefits of the collective management of wild olive forests and olive orchards in a mountain region of Nepal • Investing in wild and domesticated olive would increase household income and generate labour in Western Nepal. • Investment in olive could have long-term economic, social, and environmental positive effects on smallholders' livelihoods. • The use of local resources and species could improve the resilience and well-being of other local communities. [ABSTRACT FROM AUTHOR]

Pesticide-free but non-organic production systems have been recently introduced in public and private programs in Europe. However, a key challenge is to define what 'pesticide-free' comprises and means. This paper aims to give insights in and critical reflections on the definition and regulation of pesticide-free crop production systems in real-world programs. To this end, we review four examples of public and private pesticide-free production programs in European crop production (i.e. from France, Germany and Switzerland). Our analysis reveals several semantic and regulatory inconsistencies and potential confusion that exists around the term pesticide-free crop production, i.e. we find that it is far from clear-cut what pesticide-free production systems are. For example, programs differ with respect to which part of the crop management cycle is regulated. Moreover, all programs offer various exceptions for pesticide use. Definitions and regulations differ widely across case studies. For example, to participate in governmental programs on pesticide-free production farmers in one country have different requirements than in another. Moreover, governmental regulations not match with those in private programs and vice versa. Pesticide-free production programs can be a useful and viable addition to European agricultural and food systems. However, based on the here presented insights, we formulate eight recommendations for policy and industry how to clarify, align and harmonize pesticide-free crop production. [Display omitted] • Pesticide free but non-organic production is of increasing relevance in European agriculture. • We investigate the design of real-world examples of governmental and private pesticide free production programs. • Definitions of pesticide free production differ widely and are often inconsistent. • We draw conclusions for policy and industry how to clarify and harmonize pesticide free production programs. [ABSTRACT FROM AUTHOR]

The need for accurate data in policy design aimed at agricultural transformation cannot be overemphasized. Unfortunately, the relevance of agricultural research in addressing the needs of farmers has been questioned due to debates about appropriate methodologies and approaches for establishing research activities and, in other instances, poorly reasoned premises and paltry delineation, definition, and understanding of the system being studied. For a country like Ghana, where agricultural transformation is a prerequisite for its sustainable development, an understanding of the accuracy of farm data measurement is necessary. The objective of this study is to estimate the yield measurement error and to analyze the sources of such measurement errors among the farmers of the Guinea Savannah zone of Ghana. Two years' data for both farmer recall surveys and crop cuts were used. Descriptive statistics, regression and sensitivity analyses were done to achieve the objectives of the study. On average, farmers' recall of maize yields (1544.6 kg/ha) was lower than the estimated crop cut yields (2593.9 kg/ha), although about 11.2% of the farmers recalled higher yields than their estimated crop cut yields. The estimated average percentage error in yield measurement between crop cuts and farmer surveys was 36.4%. These yield measurement errors are due to systematic biases, including those involving the recall of farm size, and the socioeconomic conditions of the farmers. Although a crop cut is costly, it has limited bias in providing a better measure of yield than farmer recall surveys. Irrespective of the method used, however, more attention should be given to potential sources of systematic bias in the design and data collection. Moreover, for proper interpretation, yield estimates from recall surveys and crop cuts should be properly interpreted as economic yield and biological yields, respectively. This paper provided clarity on the differences in maize yield estimates in Ghana and provided measures on how to improve precision in yield data. [Display omitted] • Precise data is a prerequisite for policy design towards agricultural development and transformation. • Maize yield measured through crop cut is significantly higher than those measured through farmer recall surveys. • The interpretation of farm yield data should be properly situated within its given context. • Yield measurement error are explained by cultivated area and socioeconomic conditions of the farmers. • Crop cut yield estimation is costly but has limited biases. [ABSTRACT FROM AUTHOR]

Continued increases in global population and rising living standards in many countries are driving a surge in demand for energy and protein-rich foods. Wheat, barley, and canola are important crops that are grown and traded globally. However, climate change, geopolitical tensions and competition from other crops threaten the ability to satisfy global demand. Accurate predictions of crop production and its spatial variation can play a significant role in their reliable and efficient production, marketing, and distribution. This review examined recently published models and data used to predict wheat, barley, and canola yield to identify which factors produced the best yield predictions. A literature search was conducted across the Scopus, EBSCOhost and Web of Science databases over seven years between 2015 and 2021. Data extracted from the papers identified by the literature search were investigated using graphical and quantitative analytical techniques to determine if the type of algorithm, input data, prediction timing, output scale or extent and climate variability both in isolation and in combination affected the model's predictive ability. The literature search produced 11,908 results which was reduced to 118 papers after applying the review criteria (peer reviewed papers focussed on models predicting yield at greater than plot scale across extensive areas using accessible data). China produced almost one third of all yield prediction models over the study period and 87% of models were used to predict wheat yield. Statistical models were the most common algorithm in most regions and in total. However, there was a surge in machine learning models after 2018. They were the most common model from 2019 to 2021, with one third developed in China. The review concluded that only the choice of modelling technique and the input data had a significant effect on model performance with the machine learning techniques Random Forest, Boosting algorithms and Deep Learning models as well as process-based Light Use Efficiency models that used a combination of remotely sensed and agrometeorological data performing best. The review showed that matching the model to the available data could improve the ability to predict wheat, barley or canola yield. The use of quantitative statistical techniques in this review, should give modellers trying to predict wheat, barley or canola yield more confidence in matching their approach to the available data than previous reviews that relied on visual interpretation of data. [Display omitted] • Recent advances in models and data used to predict crop yield were examined to identify any patterns and trends. • Statistical models were the most common but there was a surge in the use of machine learning models after 2018. • Quantitative analytical techniques were used to identify factors that affected the model's predictive ability. • Machine learning models were more accurate than process-based models but not statistical models. • Combining agro-meteorological with remotely sensed data was more accurate than using agro-meteorological data only. [ABSTRACT FROM AUTHOR]

The urgency of redesigning the way food is produced and consumed has implications for the systems perspective of agronomy. An increasingly important strain of research addresses the 'how-to' question associated with the transformation of 'maldesigned' agricultural systems and produces 'transformative knowledge' as part of transitions in-the-making. Such knowledge production goes beyond 'transformation knowledge' resulting from the more classical analytical agronomic approaches and stances. Contributing to transitions in-the-making thus calls for systems agronomists to question their profession's why, what, and how. This special issue brings together contributions by systems agronomists collaborating with other scientists and societal actors revealing the less visible stream of fine-grained work to support transitions in-the-making in agroecosystems. In this introductory paper, changes in research scope and methods emerging from the contributions are highlighted to reinforce the engaged systems agronomist's roles in contributing to food system transitions. Three results stand out: 1) Contributing to transitions in-the-making necessitates going beyond analysis-oriented research stances to address the 'how' of transformative knowledge, and move beyond the more time-efficient dominant 'what' and 'why' questions; 2) Meta-methods are emerging about how to adapt and embed existing agronomic knowledge products within change processes. Reflection on the overarching transformation dynamics and on how to articulate knowledge products and change process will constitute an important extension of the systems agronomist's methods; 3) The efficacy of scientific transformative knowledge development may be enhanced if the notion of design is used more strictly in relation to engagement in societal change. The need to respond to the how-to questions associated with the transformation of the way food is produced and consumed requires systems agronomists to reflect on their stance in the research process, the way they design their research inclusively, and the way they shape their methods. This Special Issue provides promising examples of the (re-)emerging strain of engaged systems agronomists. • Special Issue shows the broadening scope and methods of systems agronomists engaged in transitions-in-the-making. • Papers provide evidence of a change in scope from transformation knowledge towards transformative knowledge development. • The articulation of knowledge and change processes constitute an important extension of the systems agronomist's toolkit. • The 'how-to' questions of transformative change call for a continuous reflexive stance on research's direction and position. [ABSTRACT FROM AUTHOR]

With the warming trend and the increasing frequency of extreme weather events, accurate crop yield estimation is becoming urgent. Crop yield estimation mainly consists of two methods: crop model simulation and remote sensing observations. Crop models can achieve accurate simulations of crop growth at field scales. However, in regional applications, they are limited by the spatial heterogeneity of certain input parameters. Remote sensing observations can obtain crop status over large areas quickly and conveniently, while lacking knowledge of crop growth processes. By combining the advantages of crop models and remote sensing, crop yield estimation with spatiotemporal continuity can be achieved using data assimilation methods. The research progress of the three elements of data assimilation system has been quantitatively reviewed in this paper. And the relevant literature was quantitatively screened and reviewed to provide a systematic overview of the application of data assimilation in crop yield estimation. In this study, the scientific background of the data assimilation system for crop yield estimations was described, and basic principles of data assimilation were introduced. A second part of this review screened and reviewed the relevant literature quantitatively. The answers to problems on: the most widely used crop model, the assimilation algorithm, and the assimilation variables were reported. Finally, a synthesis of the emerging directions and challenges of data assimilation systems for crop yield estimation were discussed. The results show that: a) the sequential assimilation method is the most widely used algorithm in the field of data assimilation, especially EnKF. b) WOFOST, DSSAT, AquaCrop and SAFY are the most common models in the research of data assimilation for yield estimation. c) In terms of assimilation variables, LAI (leaf area index), SM (soil moisture), and VIs (vegetation indexes) are relatively common assimilation variables. Research progress of data assimilation system for crop yield estimation is summarized from the aspects of algorithm improvement, model coupling research, multi-source remote sensing data assimilation and multiple assimilation variables. This review quantitatively examined and contrasted the research progress of data assimilation systems providing researchers with a more comprehensive background. It also suggests ideas and references for higher resolution, more accurate and reliable yield estimation for future research. [Display omitted] • Data assimilation is a vital tool to estimate and forecast regional crop yield. • The 143 papers on crop yield estimation by data assimilation published during 2000–2021 were searched and reviewed. • The paper focused on current models, algorithms, variables, spatial resolution and period of assimilation. • The progress of multisource and multivariable research was discussed. • The challenges and future directions were expounded. [ABSTRACT FROM AUTHOR]

From its early beginnings the Internet has been used by scientists to collaborate and share information about their research. Increasing connectivity and networking capabilities have resulted in improved collaboration functionalities ultimately combined in complete virtual research environments (VRE) as a type of virtual laboratories. These aim at providing collaborative online workplaces with access to all needed tools, data, and computing resources, and supporting data sharing. Since each research domain has its own characteristics, requirements, and preferred tooling, VRE providers must make trade-offs between the specificity of components and the functionality provided. The D4Science VRE adopts a modular approach based on open standards for constructing VREs for interested communities. The agro-climatic science domain develops diverse analytical tools that it connects to heterogenous data sources (i.e. climate data, experimental fields, satellite data, soil samples) originating from other domains, which is often poorly standardised and sparsely interlinked at best. The aim of this paper is to test and evaluate the usefulness of the D4Science based VREs for this agro-climatic science domain, using crop growth simulation and crop phenology estimation as characteristic use cases, with specific attention to Open Science. Based on the needs of the use cases a VRE has been composed and further developed in an iterative approach and evaluated at the end of each implementation cycle. Both the development work and the evaluation results point at the foreseen potential benefits of adequate VREs and the current existence of sufficient opportunities and capabilities for constructing them. The focus when developing VREs should be on supporting research with proven and stable tools, instead of striving to include the latest and greatest. The agro-climatic research domain has ambitious requirements concerning the availability and integration of data and models, which proved to be particularly challenging for incorporating in a VRE. Yet, a clear but gradual adoption of digital techniques to further the science itself is happening and VREs represent an ultimate possible end-state of Open Science. To conclude, this paper provides a few recommendations that we think can help this ongoing transition. [Display omitted] • The promise of VREs to enable collaboration is confronted with the practices of agro-climatic researchers. • Adopting a use case and evaluation approach the D4Science VRE has been adapted for use in agro-climatic research. • The results point at the potential benefits of VREs and highlight current obstacles in VREs and agro-climatic research. • The focus of VREs must be on supporting research with proven tools, instead of striving to include the latest innovations. [ABSTRACT FROM AUTHOR]

Humans have relied on agriculture to feed their communities for thousands of years. Irrigation is practiced in many different forms over the years in countries all over the world. Although modern irrigation systems have been developed, and are in use in many countries, ancient irrigation systems (AISs) have also played a major role in sustaining food production, especially in smallholder farming in least developed and developing countries. The editorial team of Agricultural Systems put out a call for a special issue on restoring AISs for sustainable agro-ecosystems development to capture ancient marvels of traditional irrigation technology across the world. The objectives of this special issue were to: (i) understand and analyse the hydrological and socio-economic networks anchored by AISs; (ii) explain the nature and sustainability of management of these systems in relation to local agro-ecosystems; (iii) analyse the implications of the AISs for land, soil and water quality, and agro-ecosystem services; (iv) qualitative and quantitative analysis of AISs, including bio-physical and bio-economic modelling of these systems; and (v) assess the feasibility of alternative technological, institutional and management strategies to enhance the productivity, profitability, and environmental sustainability of the systems. The overall goal of the special issue was to develop a useful repository for this information as well as to use the journal's international reach to share this information with the agricultural systems research community and journal readership. This paper provides reflections of papers published in the special issue. The special issue resulted in twelve high quality original research articles and one review article from Asia, Africa and Europe. The findings from various papers revealed that the AISs have been degraded due to human interventions or the anthropogenic activities across the world. Various papers emphasized that as a corrective measure, there is a need for developing and implementing rehabilitation projects in these systems. Authors identified that appropriate policy interventions by the relevant authorities would be a major step towards such rehabilitation process. However, resetting the ecosystem structure of the AISs strictly towards their historical manifestation is neither required nor feasible in the present context as it would contradict the expectations of stakeholders from these systems. The knowledge generated through the special issue provides evidence-based information on various aspects of AISs. It helps aware governments, private sectors and development agencies for improved policy planning and decision making and for prioritizing the restoration, rehabilitation, and management of various AISs around the world. [Display omitted] • AISs contribute to food security globally but evidence of constraints and issues prevailing in these systems is lacking • The objective of the VSI was to provide evidence on various aspects of AISs through original and review papers • The VSI includes 13 papers on AISs in Asia, Europe and Africa highlighting their key features, sustainability and restoration • Papers based on Village Tank Cascade Systems of Sri Lanka dominated the special issue • Their rehabilitation needs urgent attention to provide water and food security to smallholder farmers [ABSTRACT FROM AUTHOR]

Current research emphasises that agricultural innovation projects are influenced in multiple ways by the Agricultural Innovation Systems (AIS) in which they operate. Yet little attention has been paid to the reverse direction of this relationship, i.e. how agricultural innovation projects affect AIS in the course of their innovative activities. Accordingly, there are currently no tools to measure such AIS spillovers from agricultural innovation projects. This paper shows that even where agricultural innovation projects have not been designed with the explicit aim of influencing AIS they can have spillovers on the AIS in which they operate. Based on this finding, it argues that designing agricultural innovation projects in a way that maximises such positive and reduces negative AIS spillovers would be a useful tool for strengthening agricultural innovation capacities in a particular territory or sector. Based on the concept of agricultural innovation projects as Organisational Innovation Systems (OIS) that are embedded in AIS, the paper develops an analytical framework for assessing spillovers of such projects on AIS and applies it to a case study of an Operational Group in the German Federal State of Hessen. The case study shows that agricultural innovation projects may yield diverse spillovers on the AIS in which they operate. In addition to considering how agricultural innovation projects are shaped – constrained and/or enabled – by AIS, the research community on agricultural innovation should pay more attention to this side of the interrelation of AIS and agricultural innovation projects. Designing agricultural innovation projects responsibly so that spillovers on AIS are monitored can help to improve national or sectoral AIS. This paper points to an underexplored issue in research on agricultural innovation and, related to this, a thus far unused potential policy tool for improving national and sectoral AIS. It further develops the concept of innovation projects as OIS into an approach for assessing the effects of projects on AIS; an area of project assessment that until now has not been adequately covered. [Display omitted] • Agricultural innovation projects may have spillover effects on the Agricultural Innovation Systems (AIS) they operate in. • We develop a heuristic framework for assessing such AIS spillovers and apply it to a case study of an innovation project. • We find that the project yielded several effects on the AIS as spillovers from its innovative activities. • These effects included actors' knowledge gains in project administration and a strengthened local culture of cooperation. • This suggests that a spillover-sensitive design of agricultural innovation projects can be a tool for strengthening AIS. [ABSTRACT FROM AUTHOR]

The "Living Lab" (LL) model of innovation practices is increasingly applied and strongly supported by innovation policies in the agricultural sector. But while the main principles of LLs imply specific approaches to collectively implementing open innovation processes, few studies have developed an understanding of the different ways of doing so, in relation to the experimental practices on which they rely. While the specificities of agricultural LLs are beginning to be described, little is known about how they more particularly reconfigure experimentation in agricultural innovation systems. The purpose of this paper is to investigate the diversity of experimentation practices in agricultural LLs. It looks at how these practices correspond to different contextualized appropriations of an open innovation model, as translated into key principles for LL implementation. First, we produced an analytical framework that combines the principles of the LL model as described in the LL literature with generic dimensions of experimentation as analyzed in Science and Technology Studies. Second, we used this framework to analyze the diversity of experimentation practices in eighteen cases of LLs in the agricultural and food sectors. Cases were analyzed via two approaches: 1- three small and recent ongoing LLs located in France were studied during a one-year immersive observation; and 2- a collection of 15 other cases (identified in scientific and technical literature, each case corresponding to experiments that are forms of appropriation of the LL model of innovation, sometimes in several similar LLs) were analyzed, mostly based on scientific papers (describing the LLs or their outcomes), technical documents and available information such as videos online, with additional visits and discussions with LL actors for a subset of cases. Our results enable us to characterize three ideal-types of experimentation practices within agricultural LLs: 1- experimentation as a game of creativity in a predefined space; 2- experimentation as a progressive contextual adaptation for innovation adoption; and 3- experimentation as a catalyst for long-term local collective action. Each ideal-type corresponds to a particular combination of ways of (i) controlling what happens within the experimentation space, (ii) defining a perimeter for experimentation, (iii) integrating plural knowledge in problematization, and (iv) integrating various actors' evaluations of experiments. Beyond a clear distinction among ideal-types, appropriations of the LL model reveal different ways of anchoring the experimental process in an explicit temporality and situation, and of considering the construction of the LL itself as experimental and open-ended. Our analytical framework opens a new way to distinguish between various experimentation practices claiming to develop open innovation processes in agriculture. The three ideal-types that we have identified show the need to pay attention to the relation between collective experimentation practices and the LL expected outcomes. [Display omitted] • Many Living Labs are developed as new models of and for innovation processes in the agricultural sector, and transform experimentation practices. • A new analytical framework of experimentation practices within agricultural Living Labs is developed and applied. • Three widely different ideal types of experimentation practices within agricultural Living Labs are identified and described. • These ideal types promote reflexivity on the processes and outcomes targeted by actors implementing agricultural Living Labs. • Our analysis offers an understanding of how products and outcomes of innovation processes relate to experimentation practices. [ABSTRACT FROM AUTHOR]

To address sustainability challenges in agriculture, private and multi-stakeholder initiatives increasingly use sustainability indicators to monitor the sustainability impact of farms. These indicators can be part of standards for certification or assessment tools to measure farm performance. While these initiatives play an important role in navigating the sustainability transition, insight in how these governance initiatives operationalize sustainability in crop farming is lacking. This paper examines how private and multi-stakeholder governance initiatives, which increasingly regulate the sustainability of a sizeable portion of agriculture globally, operationalize the concept of sustainability. It provides understanding into which sustainability themes are commonly included in sustainability initiatives, and those that are not, which can give insight into the direction these initiatives are steering crop production worldwide. We developed an overview of governance initiatives using databases and existing research on sustainable agriculture initiatives. Documentation about these initiatives was gathered and used to systematically review descriptive and institutional characteristics of the 29 sustainability assessment and standard initiatives. Moreover, we analyzed the kinds of indicators (i.e., performance, practice, and target) and the themes that are prioritized in the environmental, social, and economic dimensions of sustainability. The analysis shows that the 29 initiatives are very diverse in their characteristics. While some specifically focus on one crop or country, others operate across production chains and at a global scale. Within specific sectors or countries, certain standards are quite influential, for instance, the Better Cotton Initiative covers 22% of global cotton production, while in Ireland, 70% of horticulture farmers are Origin Green certified. Globally, 2% of the crop land is certified under one of the 14 standards that reported land use. Our analysis of sustainability themes show an imbalance across environmental, social, an economic sustainability, with environmental sustainability being prioritized. In particular, social themes such as quality of life, food sovereignty and fair trading practices were less often included (38, 24 and 21% respectively). Sustainability standard and assessment initiatives govern a substantial and growing portion of global cropland. They play a central role in determining what is considered sustainable and thereby influence which farming practices are applied worldwide. The impact of these initiatives is likely to continue to grow as sustainability assessments and standards become more widely adopted. In examining a large sample of standard and assessment initiatives, this study presents a comprehensive overview of the current state of sustainability indicators for crop farming globally. [Display omitted] • This paper analyzes both assessment and standard initiatives in crop production. • It analyzes the operationalization of sustainability in crop farming globally. • Imbalances are found across environmental, social, and economic sustainability themes covered. • Variation in the use of practice, target, and performance metrics can be recognized. • Continuous improvement is not widely incorporated into sustainability governance. [ABSTRACT FROM AUTHOR]

Climate-smart agriculture (CSA) aims to address climate change, climate variability, and food security while sustaining productivity. The literature on acceptance and adoption of CSA technologies recognizes the importance of the policy environment in shaping farmers' decisions, particularly the policy mix, including economic, regulatory, and information instruments to stimulate CSA acceptance. Behavioral models have been used to better explain CSA acceptance, however, only a few studies integrate farmers' behavioral drivers and their policy mix appraisal, which in combination, are important determinants for the successful acceptance of CSA. This paper proposes a model that integrates behavioral drivers and policy mix appraisal influencing the acceptance of CSA technologies. We aim to examine how farmers' behavioral drivers and their appraisal of the policy environment influence the acceptance of CSA technologies and practices. We studied the Costa Rican coffee sector and conducted 523 surveys with coffee farmers and two focus groups with experts, extension agents, and cooperatives. An ordered probit model was used to identify factors explaining CSA acceptance. The results indicate that besides the influence of behavioral drivers, policy consistency and comprehensiveness and the type of instrument targeting farmers' behaviors play an important role in explaining CSA acceptance. Our results suggest that a positive appraisal of policy consistency and comprehensiveness are important for increasing farmers' acceptance of CSA and sustainable practices. This deepens earlier thinking on "policy packages" effectiveness by showing that the farmers' appraisal of the overall policy mix influences their decisions to engage with CSA. Our study shows the importance of considering system context effects (policy environment) on farmers' decision-making. Since the integration of behavioral drivers and the appraisal of the policy mix characteristics is relatively underexplored for CSA, our empirical results may help to unravel farmers' decision-making processes. Thus, it can be used for rethinking and adjusting policy interventions toward more balanced and comprehensive policy mixes, as it enables feedback from policy implementation and can further induce policy learning. [Display omitted] • We determine the factors influencing farmers' acceptance of Climate-smart technologies • CSA acceptance results from behavioral predictors, socioeconomic drivers, and policy appraisal • Policy consistency and comprehensiveness are key drivers for increasing farmers' acceptance of CSA • Integrating behavioral drivers and policy appraisal enhances the understanding of system contextual effects on farmers' decisions [ABSTRACT FROM AUTHOR]

Agricultural food production is both affected by and contributing to climate change. At the global scale, agri-food systems are responsible for one-third of total greenhouse gas emissions. With progressing climate change, the risks of crop failure increase. Thus, an urgent need is to reduce emissions from food systems while increasing their resilience to climate change. Enormous untapped potentials to achieve these dual goals lie in transforming agri-food systems towards more diverse, plant-based, and regional food production systems. In this paper, we present an innovative approach for identifying climate-adapted alternative food crops that could (1) help to diversify existing cropping systems and thus increase their climate resilience and can be (2) nutritious elements of plant-based regional diets with reduced emissions. The approach builds on the model ecocrop to select food crops that could benefit from regionally projected changes in climate. The model-based analysis is complemented with a literature review to examine the ecocrop results for their plausibility and provide a broader assessment of potentials for cultivation, utilization, and nutritional values of model-selected crops. The approach is applied to Switzerland, where we identify eight alternative crops with the potential to increase climate resilience while contributing to healthy human diets of regional consumers with benefits for climate mitigation (almond, pecan, sesame, durum wheat, quinoa, lentil, lupine, and borage). The literature review indicated that the increasing demand for many of these crops suggests great potential for regional marketing of crop products. The results produced in this study provide an initial guide for researchers and innovative farmers interested in experimenting with alternative crops in Switzerland, thus promoting climate-smart food system transformation from the production side. Using our unbiased bottom-up screening approach, we identified climate-adapted alternative crops that can provide essential nutrients, cover nutritional gaps in Switzerland, diversify existing production systems, and improve sustainability. [Display omitted] • Innovative bottom-up screening approach identifies climate-adapted alternative crops. • Almond, pecan, sesame, durum wheat, quinoa, lentil, lupine, and borage are identified. • Identified crops provide essential nutrients and cover nutritional gaps in Switzerland. • Selected crops can diversify existing production systems to improve sustainability. [ABSTRACT FROM AUTHOR]

Area-wide collaboration across private and public property boundaries can enhance the management of weeds and minimise the spread of herbicide resistance. Yet we know little about the practices individual land managers engage in to achieve area-wide weed management (AWWM). This paper uses Social Practice Theory (SPT) as a framework to understand how cropping land managers engage with the practices of AWWM, and what the drivers and barriers are to their participation. 30 qualitative interviews were undertaken with land managers in Australian cropping regions of the Darling Downs (Queensland), Gwydir (New South Wales), Riverina (New South Wales) and Sunraysia (Victoria). Thematic analysis of the interviews explored the three dimensions of SPT—meanings, materials, and competences—of AWWM and the interactions between these elements. There is a value-action gap between growers' desire to participate in AWWM practices and their capacity to do so. The analysis reveals that narrowing this gap requires interventions at the points where the SPT elements intersect. It recommends beginning by leveraging existing commonalities between growers who already have a desire to participate in AWWM then scaling out by encouraging trusted agronomist networks to link diverse growers to facilitate collaborative weed management practices. There is also a role for government in supporting the development of leadership capabilities among growers, providing an enabling environment for agronomists to act as systemic facilitators and leading by example on public land. Most of the research on collective management of weeds has focused on formal groups in grazing systems. This study provides new insights into how and why weeds are largely managed independently in cropping systems and proposes ways growers may be supported to adopt more collaborative practices to address this landscape-scale problem. • Most growers believe area-wide weed management (AWWM) would be beneficial. • Few growers participate in AWWM: there is a value-action gap. • Growers are confident they have the competences needed to manage weeds on their own. • Lack of action from neighbouring private and public land managers undermines confidence in AWWM. • Diverse meanings about autonomy and financial imperatives create contradictions that constrain AWWM. [ABSTRACT FROM AUTHOR]

To contribute to building sustainable and effective climate change adaptation solutions avoiding usability gap, it is largely recommended to engage in the process of co-production, integrating expertise and knowledge from various academic and non-academic actors. We want to learn if and how co-production, believed to effectively link knowledge and decision-making, and thus suggested as the best practice in building decision-support frameworks, is really applied in the frameworks that are being implemented on the ground. A literature review allowed us to identify integrated decision-support frameworks for climate-change adaptation in agriculture developed and used over the period of the last 10 years and involving non-academic stakeholders. To analyse them, we chose as an assessment tool the four co-production principles proposed by Norström and colleagues: context-based, pluralistic, goal-oriented and interactive. The useful entry points for incorporating co-production in the design of decision-support that we found in the reviewed frameworks include among the others adequate participants selection strategy, building on existing interaction spaces, developing a theory of change with the participants, and involving participants in the design of different elements of the method. The architectures of the analyzed frameworks contained more elements that responded to pluralistic and interactive principles than to context-based and goal oriented principles, we have also identified gaps in the design, such as taking into account the personal characteristics of researchers that could strengthen a framework's implementation and its impact, or attempts at bridging different levels of decision making, to cover the triad of science, policy and practice. A detailed look at the decision-frameworks that are actually being applied allows for a critical reflection whether and how we as researchers use what we preach as an effective way of responding to sustainability challenges in agriculture. Co-production principles turn out to be a useful tool for analysis and we suggest they can be used as a check-list when designing decision-support frameworks for climate-change adaptation. This papers offers useful examples of how to shift the research-led processes of decision-support towards more co-production with non-academic actors, to increase chances of bridging the gaps between science, policy and practice. [Display omitted] • Knowledge co-production is largely suggested as the best practice to bridge the gaps between science, policy and practice. • Evidence is lacking for the actual use of co-production in decision-support for climate-change adaptation in agriculture. • We identify useful entry points for applying co-production principles from already used decision-support frameworks. • Researchers are rarely selected to build decision-support frameworks based on their ability to support co-production. • When designing decision-support frameworks co-production principles can be used as a guiding tool. [ABSTRACT FROM AUTHOR]

Sustainability-Oriented Innovation (SOI) is recognized as a way to address agrifood system sustainability challenges. Because of its complexity, SOI requires inter-organizational collaboration between actors within and around value chains. Since farming practices account for a large part of the environmental impact of food products, farmer involvement in SOI processes is key. However, there is a lack of evidence on the degree of farmer engagement in inter-organizational collaboration, as well as the diversity of partners involved. Moreover, our understanding of the heterogeneity of collaboration in relation to the sustainability dimensions of the innovation SOI processes is limited. The objectives of our research are twofold. First, to propose a novel conceptualization of SOI, converging the sustainability specificities of the innovation process and the characteristics of the collaboration supporting the innovation process. Second, to unpack heterogeneous forms of collaboration in SOI with attention to farmer engagement in these forms. We developed the SOI framework and analyzed SOI processes in the fruit and vegetable sector across Europe. A multiple correspondence statistical analysis was carried out based on data from more than one hundred value chains that have implemented a SOI process. A hierarchical clustering analysis was performed to reveal patterns of collaboration in SOI. The results show different patterns of collaborative SOI in European fruit and vegetable chains. The SOI differ as regards to three specific dimensions: the diversity, the direction, and the distribution; and they rely on various collaboration forms. Seven clusters are analyzed and discussed, with specific attention to the farmer engagement in the collaboration forms. We differentiated between 1) Intermediary-led farm & food innovation with farmer collaboration; 2) Intermediary-led short food supply chain innovation with farmer collaboration; 3) Processor-led green food product innovation without farmer collaboration; 4) Farmer-led sustainable agricultural practices innovation with a collaborative network; 5) Farmer-led coupled innovation with downstream integration; 6) value chain actor-led coupled innovation with farmer collaboration; 7) Retailer-led distribution innovation without farmer collaboration. The relationship between diverse collaboration forms and SOI dimensions is discussed. Revealing the heterogeneity of collaboration forms in SOI processes is important for developing sound policies. Given the significant role of farmers in sustainable agrifood system transitions, this paper reveals various levels of farmer engagement in collaborative SOI processes in value chains. Results have implications for mission-oriented policies since they allow a more precise and targeted approach to agrifood chains within the broader agrifood system transformation. [Display omitted] • Sustainability-oriented innovation (SOI) has three dimensions: direction, diversity, and distribution • SOI requires collaboration among multiple actors within and around the value chain • SOI is characterised in 74 food value chains in Europe; seven clusters reveal the heterogeneity of collaboration • The relationship between heterogeneity of collaboration and SOI dimensions provides insights for targeted innovation policy [ABSTRACT FROM AUTHOR]

Sustained high yields of tea rely on the supply of nitrogen (N) from soil reserves, typically maintained by N fertilisation from inorganic or organic sources. This paper describes how soil N levels, including the effects of soil organic content and pH, were developed and incorporated into a crop yield simulation model called CUPPA-Tea. The nitrogen dynamics are presented in terms of i) the initial nitrogen stocks, ii) the addition of nitrogen to the system, iii) the uptake, use and loss of nitrogen by tea plants, and iv) nitrogen flows within the soil. CUPPA-Tea was then calibrated and validated using measured tea yields from Tanzania and Kenya. After integrating a wide range of nitrogen algorithms, the model explained 79% of the variation in annual yields within a nitrogen and irrigation experiment in Tanzania and a fertilizer experiment in Kenya. The slope of the relationship was 0.84 and 0.73 respectively, the root mean square error was 660 kg ha−1 and 507 kg ha−1, and the modelling efficiency was 0.77 and 0.75 respectively. The model predicted that in the absence of N application, tea yields would be higher from a site with a high rather than a low soil organic content. By contrast, at high levels of mineral N application, the yield response in the model was not sensitive to the soil organic content. Hence within the model, a site in Tanzania with a low soil organic content of 1.6% showed a greater yield response to applied mineral N than a site in Kenya where the soil organic carbon was 4.0%. The model also predicted small losses of N from the cropping system through denitrification and leaching due to the acidic soil conditions (pH < 4.5) and an assumed tea rooting depth between 300 and 500 cm. In Tanzania, irrigation was predicted to result in around 10% higher nitrogen uptake than under unirrigated conditions. The use of the CUPPA-Tea model can be useful in supporting decision making and improving the accuracy of tea yield estimates, as well as predictions of N fate within the soil-plant-atmosphere continuum. [Display omitted] • N dynamics, including the effects of soil organic content and pH, were developed and incorporated into the CUPPA-Tea model. • The resulting model explained 79% of the variation in tea yields from experiments in Tanzania and Kenya. • Under no N fertilizer, the model predicted greater yields in a site with a high rather than a low soil organic content. • Minimal N losses due to volatilisation, denitrification, and leaching were predicted (at pH 4.0–4.5 and rooting depths 3–5 m) • CUPPA-Tea was also used to identify the financially optimum N fertilizer application rate in pruned and unpruned tea. [ABSTRACT FROM AUTHOR]

India's agricultural systems are increasingly affected by climate change's adverse effects. The Government of India has an impressive set of programs to address this issue, but they have substantial shortcomings, especially in reaching women farmers. We aim to understand policy and implementation gaps in reaching women farmers with climate-smart agriculture (CSA) practices and study how women and men farmer's different roles in agriculture shape their needs and access to complementary services needed to adapt to climate change. An extensive review of India's agriculture and climate policies and program and a series of focus group discussions with farmers in Gujarat, India to discuss constraints and potential entry points for better reaching women farmers with CSA practices. Women's increased vulnerability to climate change and reduced access to CSA practices can be attributed to limited land ownership, poor access to credit, reduced access to information and formal extension, and multiple pressures on their time. Village cooperatives and self-help groups can be leveraged to support women's access to agricultural information and adoption of CSA practices. This paper highlights constraints to information and extension access by Indian women farmers that could impede the widespread adoption of CSA practices. It fills an important knowledge gap in designing gender-responsive policies and inclusive agricultural extension systems to promote adoption of CSA practices among smallholder farmers. [Display omitted] • Women farmers have limited land ownership, access to credit, access to information and formal extension, and time, constraining adoption of climate smart practices. • Building gender-responsive extension systems is critical for promoting adoption of climate smart agriculture practices. • Knowledge of gendered experience of climate change, availability of gender-disaggregated data and gendered analyses are important aspects. • Participatory approaches to policy making and involvement of local and community organizations needs to be strengthened in India. • Village cooperatives and self-help groups can be key intermediary organizations to support women's access to information and adoption of CSA practices. [ABSTRACT FROM AUTHOR]

Pastoral and agro-pastoral (PAP) systems in East Africa face a range of challenges including increased climate variability. Various measures have been proposed to improve the resilience of pastoral/agro-pastoral (PAP) systems to drought. However, identifying the most effective measure for a given system and location is complicated, and tools are required to appraise measures on a consistent basis. This paper develops a model of a PAP system and uses it to assess the effects of four measures (Index-based livestock insurance, IBLI; Commercial destocking with an early warning system, EWS; Rangeland restoration, RR; Fodder planting, FP) on the resilience of the PAP system. It also quantifies the greenhouse gas (GHG) effects of the measures, thereby identifying potential trade-offs and synergies between the policy objectives of resilience and climate smart agriculture (CSA). A dynamic model of the Borena pastoral cattle system was developed to undertake the analysis. At its core is a herd model that calculates the changes in cattle population over time. Feed availability and drought occurrence affect fertility and mortality rates, which in turn determine the population and (meat and milk) production. A suite of indicators covering the three dimensions of CSA (increasing productivity, enhancing resilience and reducing GHG emissions) were developed, and used to compare the situation with and without measures. Destocking with an early warning system provides the biggest increases (relative to the no measure situation) in production and profit, due to the way it changes the herd size and structure. It maintains a larger herd than any of the other measures, and a greater proportion of the herd are adult females. Fodder planting and rangeland restoration provide moderate increases in production and profit. Index-based livestock insurance provides a moderate increase in protein production, but has no effect on profit, as it is designed to reduce risk rather than increase productivity or profit, at least in the short term. All of the measures increase the total emissions relative to the no measure scenario. In terms of the three dimensions of climate-smart agriculture, IBLI leads to some improvements in productivity and resilience but leads to large increases in total emissions, and modest increases in emissions intensity (EI). EWS leads to large increases in productivity and resilience. However, it also leads to large increases in total emissions and a mixed effect on EI. FP and RR improve productivity and increase total emissions, while having little effect on EI or resilience. This paper illustrates the way in which systems dynamic model can be used to appraise measures designed to improve resilience. The result identify potential synergies and tensions between the goals of resilience and climate smart agriculture, and raises the question of whether fully climate-smart goals are viable in these systems. [Display omitted] • A model of a pastoral cattle system in Ethiopia was developed and used to investigate resilience. • Four measures were analysed: livestock insurance; managed destocking; rangeland restoration; fodder planting. • Destocking provides the biggest increase in production and profit, due to the way it changes the herd size and structure. • Fodder planting and rangeland restoration increase production and profit. Insurance increases production but not profit. • All of the measures increase the total GHG emissions but lead to little change in emissions intensity. [ABSTRACT FROM AUTHOR]

Narratives dominate the agricultural discourse that digitalisation is the 'silver bullet' to agricultural, environmental, and global issues, resulting in an external push towards automation and a rapid increase in digital technologies in the sector. Concentrated productivist views and techno-optimist hype and momentum is carrying us briskly towards a digital farming revolution, with little conversation or consideration of the social impacts of digitalisation. The application of social science research to digital agriculture is relatively new and the pace at which it has been developing to keep up with digital advances in the sector has left this body of literature scattered and lacking sufficient overview. To address this, a scoping study was conducted on social and behavioural science literature related to digital agriculture. This scoping study, which incorporates 200 references, pays particular attention to stakeholder engagement and how agricultural digitalisation has been developing. This methodology enables us to provide an extensive overview of this field of research, presenting key themes pertaining to the literature including barriers and facilitators of, as well as anticipated positive and negative impacts of digital technology adoption. We find that whilst there has been a general win-win, techno-positive narrative in the agricultural sector, proof of these benefits is limited and some technologies are eliciting negative effects to its users, transforming the landscape of agriculture. In order to be responsive to these negative consequences, it is important that we turn our attention upstream to examine how technologies are designed and developed for use in an agricultural context. This can be achieved by integrating more inclusive approaches to technology design. These approaches will help to mitigate negative effects of technologies, help to create successful and responsible innovations, tackle low adoption issues, and help to create more responsible digital futures. [Display omitted] • There is a lack of overview of how digital agriculture is developing. • A scoping study was conducted on 200 social science papers relative to digital agriculture. • Digital technologies elicit both positive and negative effects. • Stakeholder engagement is predominantly used to examine or establish reasons as to why farmers are not adopting technology. • A more inclusive approach to technology design and development is needed to mitigate negative effects of technologies. [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]

Ancient irrigation systems (AISs) have been providing a multitude of ecosystem services to rural farming and urban communities in Asia and Africa, especially in arid and semi-arid climatic areas with low rainfall. Many AISs, however have now been degraded. A systematic analysis of AISs on their typologies, causes of degradation, and their ecosystem services is lacking. The objective of this review was to synthesize the knowledge on AISs on their typologies, status and causes of degradation, ecosystem services and functions, and identify gaps in research in Asia and Africa. A critical review of peer-reviewed journal papers, conference and workshop proceedings, book chapters, grey literature, and country reports was conducted. Qualitative and quantitative information from journal papers were used to conceptualize the typologies and analyze the status and causes of degradation, and ecosystems services and functions provided by the AISs. Based on the review, we classified AISs into three groups by source of irrigation water: Rainwater harvesting system (RHS) with small reservoirs, ground water based system, and floodwater based system. The RHSs, which used to receive reliable rainfall and managed by well cohesive social organizations for their maintenance and functioning in past, have now been silting due to extreme rainfall pattern and breakdown of the cohesive organizations in recent decades. In ground water based systems, indiscriminate development of deep tube wells causing siltation of channels has been a major challenge. In floodwater irrigation systems, irregular rainfall in the highlands and the breakage of irrigation structures by destructive floods were the main causes of degradation. Lack of maintenance and increased soil erosion, inadequate skilled manpower, and declining support from the government for repair and maintenance were the main causes of degradation of all AISs. The main ecosystem service provided by all AISs is water for agriculture. In tank- and pond-based systems, fish farming is also practiced. Tank irrigation systems provide various types of provisioning, regulatory, cultural and supporting services, especially in India and Sri Lanka. Ground water based systems provide water for domestic purposes and various cultural services. Floodwater based systems provide water for power generation and wildlife habitat maintenance and help in flood control. The knowledge generated through the review provide evidence-based information, and help aware governments, private sectors and development agencies for improved policy planning and decision making, and prioritizing the restoration, rehabilitation, and management of various AISs. Community Managed Ancient Irrigation Systems in Asia and Africa: Typologies, Degradation and Ecosystem services. [Display omitted] • Ancient irrigation systems (AISs) in Asia and Africa provide a multitude of benefits to mankind but are at a threat of degradation. • A systematic review conducted to identify typologies, status and causes of degradation of, and ecosystems services provided by, AISs. • Three typologies (rainwater harvesting, groundwater, and surface water based) and four ecosystems services identified. • Cascade tanks in Sri Lanka and India provide irrigation water for farming communities and ecosystem services for local communities and societies. • Rehabilitation of AISs needs urgent attention by governments, private sector, and development partners. [ABSTRACT FROM AUTHOR]