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• Considering pistachio, a supply chain network is firstly designed in this paper. • A mixed linear mathematical model is formulated based on the proposed problem. • Efficient and well-known old and recent meta -heuristic algorithms are utilized. • Three hybrid meta -heuristic algorithms are developed. • The quality of the proposed algorithms is investigated and compared. Nowadays, recent advances and developments in the agricultural sector have raised concerns regarding the environmental impact of agricultural wastes and the safety of agricultural products. Recently, reverse logistics have been used to address these issues. It is also possible to reduce the environmental impact of these by-products and return them to the network by opening compost centers and designing an optimal supply chain distribution network. In this paper, a new mixed linear mathematical model is developed for an agriculture supply chain network to minimize the total fixed and variable costs of the closed-loop supply chain,. To address the proposed model, efficient and well-known old and recent meta -heuristic algorithms are utilized. Besides, to boost intensification and diversification phases, three hybrid meta -heuristic algorithms are developed. Finally, the quality of the meta -heuristic and hybrid algorithms are investigated and compared. [ABSTRACT FROM AUTHOR]

To promote green agricultural production, the government often provides two types of subsidies: the Green Technology Operation Subsidy (GOS), and the Green Technology Investment Subsidy (GIS). This paper examines the impact of government subsidy policies on a two-tier agricultural supply chain in which the farmer can choose to self-invest in green technologies or outsource them to the service provider. Our results indicate that outsourcing is more conducive to higher green technology inputs than self-directed investment in most cases and can generate higher profits for small-scale farmers; however, as the land scale increases, farmers prefer to invest in green technologies by themselves. In addition, if the farmer decides to self-invest in green technologies, the farmer prefer the GIS (GOS) policy when the land scale is small (large). Further analysis shows that if the farmer decides to outsource green technologies, the farmer's profit under the two subsidy policies is equal when the land scale is small, while the farmer benefits more from the GOS policy when the land scale is large. Finally, from the perspective of promoting agricultural green production, the GIS policy is always better than the GOS policy, regardless of whether the farmer outsource green technologies. • Investigate the production decision of farmers when outsourcing green technologies. • Outsourcing generates higher profits for small-scale farmers than self-investment. • Farmers benefits more from green technology operation subsidy when land scale is large. • Outsourcing is in most cases more effective than self-investment in increasing green technology inputs. • Green technology investment subsidy yields higher levels of agricultural green production. [ABSTRACT FROM AUTHOR]

The waste heat recovery technology of data center has attracted the attentions for energy conservation and emission reduction in recent years. This paper presents a new scheme which uses heat recovery technology to link the data center and the agricultural sector. The detailed system design, technical validation, and environmental and economic analysis show that the project can fully absorb a large amount of waste heat in the data center, and use the air source heat pump (ASHP) to transport heat to an ecological farm, reducing the consumption of electric energy and promoting the development of agriculture and animal husbandry. In a case study of China Brain Green Data Center, the dynamic payback period is about 3.4 years, and the cumulative net present value will reach $666 k. In terms of environmental impact, the proposed ASHP-based complementary utilization scheme can save 676 MWh of electricity for the data center and 88 MWh of electricity for the ecological farm in each year, which is equivalent to reducing coal by 230 tons and reducing CO 2 emissions by 168 tons. Therefore, from the perspective of economic and environmental benefits, this new complementary waste heat utilization technology realizes a win-win development. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) are pivotal for real-time monitoring in diverse sectors through the Internet of Things (IoT). However, current wireless communication protocols face challenges in integrating Precision Agriculture (PA) management systems with Artificial Intelligence (AI). PA optimizes nutrient supply and pesticide use, enhancing crop yield and product quality. Despite these advantages, structural changes in WSNs, including deployment, coverage, scalability, and energy consumption, hinder their potential. This paper conducts a comparative study of WSNs in agriculture, focusing on wireless protocols, energy harvesting, and efficiency procedures. The research delves into challenges faced by WSNs in agriculture, emphasizing the need for power-saving agricultural management strategies to ensure sustained, effective long-term monitoring. The study addresses integration issues, explores agricultural benefits, and proposes solutions to enhance WSN performance in dynamic field environments. [ABSTRACT FROM AUTHOR]

• Develop a probability model for the poor farmers to escaping poverty. • Analyze three poverty alleviation schemes conducted by the pro-poor enterprise. • Examine the compensation strategy of different poverty alleviation schemes. • Provide valuable insights into the supply chain and poverty alleviation policy. In this paper, we examine the minimum procurement price and government subsidies for the successful implementation of three Poverty Alleviation Schemes (PASs): Contract-Only Scheme (CO), Contract with Compensation Scheme (CS), and Capacity-building with Training (CT). We consider an agricultural supply chain with a pro-poor enterprise and a group of impoverished farmers who grow, harvest, and sell the product to the pro-poor enterprise, and the government decides to subsidize the pro-poor enterprise for poverty alleviation. The price of the agricultural product and the harvest output are uncertain and negatively correlated. We consider a new type of poverty alleviation objective: the poor farmer escapes poverty if their profit exceeds the poverty line (PEP) and establishes a higher probability of escaping poverty (EPP). We use real coffee and tea prices to capture the skewness of the distribution of product prices in a numerical analysis. We find that (i) the impoverished farmers would benefit from PASs, but the pro-poor enterprise would incur risks because both parties share the uncertainty of harvest output in PASs; (ii) the local government should provide subsidies to the pro-poor enterprise to implement PASs better; (iii) CT and CS have a lower bound for the procurement price as well as local government compensation; and (iv) if the product price exhibits a left-skewed distribution, then the EPP becomes crucial, and a higher procurement price is required to increase the EPP. [ABSTRACT FROM AUTHOR]

Central kitchen engineering for industrial production of prepared dishes is gaining prominence in response to urbanization, socioeconomic shifts, evolving family structures, and COVID-19 impacts. These dishes, including staple foods, convenience items, fresh-cut vegetables, and sides, cater to the demand for convenient, diverse, and health-conscious options. Central kitchen engineering significantly enhances production efficiency, reduces labor and space requirements, and advances food industrialization. However, central kitchen engineering still faces challenges such as conceptual clarity, outdated technology, sensory quality decline, inconsistent standards, and heightened nutrition and safety concerns. To enhance quality and safety, research efforts are currently focusing on innovative and emerging technologies. Innovations cover cooking/reheating techniques, cooling/freezing methods, sterilization, packaging, natural extracts, and fat substitutes. The current state, research progress, and innovations in prepared dishes are critically analyzed in this paper, offering insights into future directions. By addressing challenges and presenting growth opportunities, this study aims to guide both academic researchers and industry practitioners, fostering progress and innovation in this dynamic field. Panoramic view of the prepared dish industry based on central kitchen engineering: the comprehensive amalgamation of raw material primary processing at the forefront, agricultural product deep processing in the middle, proceeding with catering and food logistics, and food market consumption and supply at the rear end, along with future opportunities. [Display omitted] • Urbanization, evolving families, and COVID-19 drive prepared dishes' popularity. • Critically analyze challenges in processing, from outdated technology to safety concerns. • Research and production tackles challenges with innovative and emerging technologies. • Roadmap for prepared dish progress is proposed for greater safety, convenience and nutrition. [ABSTRACT FROM AUTHOR]

The adoption of soil and water conservation (SWC) technology is a key strategy for reducing global land degradation and improving agricultural productivity. This study uses survey data from households in the Loess Plateau in 2017 to evaluate the impact of social interaction on the decision-making process of SWC adoption by farmers. It measures the mediating effect of information acquisition and the moderating effect of internet use and deploys the probit model, general decomposition (KHB) model, and moderated effect model for analysis. The study found that the level of SWC adoption by farmers in the Loess Plateau of China remains relatively low, with only 57.3% of farmers adopting SWC, whereas social interaction increases the possibility of farmers adopting SWC by 10.0%. Accordingly, the paper argues that social interaction can encourage farmers to adopt SWC by improving their ability to acquire information, while internet use can positively moderate the impact of social interaction on farmers' adoption of SWC. Furthermore, the study found the positive impact of social interaction on SWC adoption is more pronounced for farmers in larger villages and those who join cooperative societies. Therefore, the research results provide a new perspective for promoting SWC in China, emphasizing the importance of enhancing social interaction improving farmers' ability to acquire information, and accelerating internet use. • Social interaction plays a crucial role in promoting SWC adoption. • Improving farmers' ability to acquire information can promote SWC adoption. • Internet use can moderate the impact of social interaction on SWC adoption. [ABSTRACT FROM AUTHOR]

A consortium managed project in the Upper Indus Basin of Gilgit-Baltistan , Pakistan, supported the creation of women's collective farming. The component of the project related to water resource management for agriculture was implemented in Hunza district, where in three sites, the communities encouraged women farmers to engage in the pilot interventions related to innovative water resource management technologies and improved farming practices to overcome climate-induced issues for farmers. This paper analyzes the situation of women as collective farmers and its contributions to women's agency. The study used a gender analysis framework, and data was collected through quantitative and qualitative research methodologies in the project site areas. The study's findings show that climate change has created issues more for women farmers being at the forefront in handling and tackling the impacts through various measures. It shows that collective farming and networking is an appropriate tool for women to challenge norms and stereotypes that hinder them from being part of the decision-making at various levels. Furthermore, it helps women to minimize drudgery by sharing burdens, using appropriate technology, and sharing their successes and issues while working together. Based on the findings, this study suggests that policy decisions to implement such initiatives at a more macro level will help involve women at various levels of decision-making, minimize their drudgery, expand the choices of women farmers, and overall contribute to the agency. [ABSTRACT FROM AUTHOR]

[Display omitted] • An advanced control method has been proposed for the boost PFC converter for smart farming lighting application. • Enhanced second-order generalised integrator filter-based quasi type-1 PLL is developed as the grid detection scheme. • Nonlinear proportional-integral controllers are applied for the voltage and current control loops. • Comparative results across several practical challenging test scenarios showed the effectiveness of the proposed method over conventional counterparts. Controlled environment farming has become an attractive solution to increase the food production with limited use of resources. LED lights are often used in this type of farming to increase plant yield. LED lights are dc load, whereas the grid provides ac power. This necessitates the use of power factor correction (PFC) converter as an interface between the grid and the lights. This paper proposes an innovative control method for the boost PFC converter, where an advanced phase-locked loop scheme is developed that can eliminate measurement dc offset and provide harmonic robust estimation of the grid voltage fundamental component. The applied nonlinear control uses the existing two-loop control architecture. Based on a baseline PI controller, a nonlinear function is added to make the controller react faster when it is far from the reference and vice-versa. Comprehensive simulation studies are conducted to assess the performance of the proposed method under various challenging test scenarios. Compared to the baseline method, the proposed technique achieved 42 % ∼ 65 % reduction in total harmonic distortion depending on the test cases, which makes the technique a suitable candidate for improving the operational efficiency and, consequently, the running cost of a smart farming lighting system on an industrial scale. Results show the effectiveness of the proposed method over the conventional counterpart. [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]

The proposal of the Data Act containing 'harmonised rules on fair access and use of data' was released on 23 February 2022. It is a horizontal framework for data access to unlock competition and innovation. The emerging Digital Agriculture sector is one of the non-personal data-driven industries that can be affected by this legislative intervention. Therefore, it is highly relevant to investigate the possible implications of the horizontal Data Act proposal from the sectoral perspective. This paper aims to map the prominent data access problems in this emerging sector, explain the needed solutions for the sectoral challenges, demonstrate what the Data Act brings, and discuss to what extent this horizontal framework is helpful to overcome the sectoral problems connected to the ambiguities on agricultural data access. Thus, this piece also identifies the remaining issues that may need to be addressed by a possible follow-up sectoral regulation and competition law. [ABSTRACT FROM AUTHOR]

This paper surveys the supportive technologies currently available for ground mobile robots used for autonomous mapping in agriculture. Unlike previous reviews, we describe state-of-the-art approaches and technologies aimed at extracting information from agricultural environments, not only for navigation purposes but especially for mapping and monitoring. The state-of-the-art platforms and sensors, the modern localization techniques, the navigation and path planning approaches, as well as the potentialities of artificial intelligence towards autonomous mapping in agriculture are analyzed. According to the findings of this review, many examples of recent mobile robots provide full navigation and autonomous mapping capability. Significant resources are currently devoted to this research area, in order to further improve mobile robot capabilities in this complex and challenging field. • We review hardware technologies for autonomous robotic mapping in agriculture. • Modern localization and mapping approaches for mobile robotics are discussed. • Path planning strategies for autonomous navigation in agriculture are analyzed. • The role of artificial intelligence for robotic mapping and monitoring is explored. • Future trends and challenges for robotic mapping in agriculture are highlighted. [ABSTRACT FROM AUTHOR]

In smart farming, information and communication technologies such as IoT are driving the agricultural industry towards improved efficiency and productivity while reducing human intervention. As agricultural systems become increasingly connected to the Internet, they provide many new opportunities but also introduce new challenges. Connecting such systems to a network exposes them to cyberattacks and vulnerabilities; therefore, security and privacy are important challenges that should be addressed. Specifically, because of the distinct characteristics of smart farming such as environmental and farm conditions, security solutions suitable for smart farming are indispensable. In this paper, we propose a location verification (LV) protocol as a security solution for autonomous and intelligent moving devices such as drones and robots deployed in smart farming. Moreover, as an application of the proposed LV protocol, we propose a blockchain-based secure drone rental mechanism for smart farming. We present a security analysis in which the proposed LV protocol and rental mechanism are secure against man-in-the-middle, impersonation, modification, replay, and relay attacks, with the rental mechanism providing mutual authentication. Through a performance evaluation of the LV protocol in terms of the communication overhead, computation cost, and storage cost and a performance evaluation of the blockchain for drone rental with regard to throughput and latency, we confirm the feasibility and efficiency of the LV protocol and drone rental mechanism in the smart farming. • A location verification protocol for agricultural drones and robots is proposed. • A blockchain-based drone rental mechanism without human intervention is introduced. • Both the protocol and the mechanism are secure against various cyberattacks. • Performance results show their feasibility and efficiency in smart farming. [ABSTRACT FROM AUTHOR]

This paper investigates the empirical linkages between production risks and the adoption of modern inputs among smallholder farmers in Tanzania and Uganda using household panel LSMS-ISA datasets. Applying a moment-based approach and a Mundlak-Chamberlain IV fixed effects model to control for unobserved heterogeneity and endogeneity, the paper uses a translog production function to estimate the mean, variance, skewness, and kurtosis of production. These estimated moments of production are then included in a multivariate adoption model to assess their effects on input adoption decisions. Results reveal that the first four moments of production significantly explain changes in the probability of adopting chemical fertilizer, improved seeds, and pesticides. While the use of these modern inputs is found to be risk decreasing, estimates suggest that the higher their purchasing costs, the greater the cost of farmers’ private risk bearing. Under the assumption of a moderate risk aversion, the risk premium amounts to 8.7% and 13.7% of the expected production revenues in Tanzania and Uganda, respectively. This willingness-to-pay is largely explained by production volatility and downside risk aversion and to a small extent by kurtosis aversion. The findings underscore the need to account for farmers’ preferences towards higher order moments when designing and implementing modern inputs’ adoption policies in developing countries. [ABSTRACT FROM AUTHOR]

The recent modernization of 1.1 Mha of irrigated land in Spain calls for the evaluation of these transformations in terms of environmental impact and resource use efficiency. The available data for this evaluation has increased with the transformation (better, digital and spatially distributed data) allowing for the use of distributed soil water and solute movement models. But most hydrological models require soil hydrologic properties that are costly and time-consuming to gather and soil information in Spain is generally scarce. This paper focuses in analyzing the soil hydrologic features in La Violada Irrigation District (VID; a 5234 ha semi-arid irrigated area recently modernized in northeast Spain) usable in soil water models for the evaluation of the new irrigation system. The recent soil map of the VID (presented in a companion paper) gathered the hydrologic and salinity properties of the horizons in the described soil units. The hydraulic conductivity (K s ) of the horizons was also assessed by the inverse auger-hole method. From these data, the VID was disaggregated in three homogeneous units according to their hydrologic features and Pedotransfer Functions (PTFs) were built for the whole VID (General Model) and separately for the homogeneous soil units (Distributed Model). These PTFs allowed for obtaining field capacity (FC) and wilting point (WP) from texture and organic matter, while K s depended upon texture and gypsum content. Apparently, there were no salinity issues in VID soils due to irrigation. The high Ca 2 + and Mg 2 + levels in the saturation extract resulted in generally low SAR, what along with the high gypsum and carbonate contents may help to prevent soil degradation by sodicity. As a result, the homogeneous hydrologic zones defined in VID may be used to recommend specific irrigation practices and as the basis for the application of distributed soil water movement models. These hydrologic properties may be applied directly as inputs to the models while the PTFs may allow for setting adequate parameters in nearby areas with similar soils from more readily available soil information (texture, organic matter and gypsum). [ABSTRACT FROM AUTHOR]

The knowledge of the soil variability is essential for its hydrologic characterization, for irrigation management and planning and especially for water movement modeling. Nevertheless, the scarcity of soil information in Spain precludes its use in irrigation management and, particularly, in the on-going irrigation modernization process. The Violada Irrigation District (VID) comprises 5234 ha and is located in a semi-arid gypsum-rich region in northeast Spain. VID has been under flood irrigation since the mid-20th Century and has been modernized recently to sprinkler irrigation. Since the early 1980s water balances in VID have been based on average soil properties and bulk irrigation data. The current irrigation system provides detailed irrigation data at plot level, thus allowing for performing distributed balances (in hydrologic homogenous zones) that require detailed or semi-detailed soil information in the form of a soil map. The objective of this two-part study is to define homogeneous zones in relation to hydrological properties relevant for irrigation management, first defining the soil variability within the VID and then analyzing the soils hydrologic and salinity features (in a companion paper). The specific objectives of this paper were (i) to draw the VID semi-detailed soil map; (ii) to explain the variability of soil properties; and (iii) to map the main soil characteristics related to irrigation management. Thirty four soil pits, located on all the different geomorphological areas, were opened and described. Samples were taken from all the horizons and analyzed for chemical and physical properties. The limits of soil units were drawn with the aid of 32 additional auger holes, the data from 72 pits opened during the irrigation transformation works, and all the previous soil information available. Finally the soils were classified down to family level. The main soil formation processes in VID were carbonation and gypsification. Thirteen soil units were defined, included in five soil subgroups and six particle size families. “Fine” particle sizes were dominant along the gypsum-rich valley bottoms. Coarser textures were found on the heights and showed higher CaCO 3 equivalent content and no presence of gypsum accumulations. A priori, the main limitation for irrigation in the valley soils was the limited drainage (due to fine textures and reduced slope) while in the highland soils it might be constrained by their low retention capacity. The high gypsum and carbonate content of the VID soils may prevent the development of infiltration problems. The soil properties map in this work will be the basis for a companion paper delineating the hydrologic properties of the soils, the basis for improved soil water balances. [ABSTRACT FROM AUTHOR]

The application of machine learning to agriculture is currently experiencing a “surge of interest” from the academic community as well as practitioners from industry. This increased attention has produced a number of differing approaches that use varying machine learning frameworks. It is arguable that Bayesian Networks are particularly suited to agricultural research due to their ability to reason with incomplete information and incorporate new information. Bayesian Networks are currently underrepresented in the machine learning applied to agriculture research literature, and to date there are no survey papers that currently centralize the state of the art. The aim of this paper is rectify the lack of a survey paper in this area by providing a self-contained resource that will: centralize the current state of the art, document the historical progression of Bayesian Networks in agriculture and indicate possible future lines of research as well as providing an introduction to Bayesian Networks for researchers who are new to the area. [ABSTRACT FROM AUTHOR]

Innovations are regarded as critical to improving the efficiency, productivity and effectiveness of African agriculture. However, few efforts have been directed at understanding ‘agricultural innovators’, especially among smallholder farmers in sub-Saharan Africa who face low agricultural productivity and widespread food insecurity. This paper investigates the profile of innovators from a local perspective in a semi-arid smallholder farming area in south-west Zimbabwe. The paper is based on data collected from key informant interviews and a household questionnaire survey administered to 239 households from Gwanda and Insiza districts between 2013 and 2014. Qualities or attributes of an innovator (which constitute the profile of an innovator) identified by key informants included: resource endowment; social networks; education; and enthusiasm (passionate and hardworking). The attributes were used in a logit regression model to estimate the probability of the 239 households exhibiting the attributes of an innovator. Social networks and resource endowment, as depicted by amount of land cultivated, were found to significantly influence the probability of an individual being an innovator. Interestingly, the common attributes of education or belonging to an innovation platform used by extension and development agents, were found not to influence the probability of one being an innovator. The paper concludes that understanding local perceptions of innovators, which is based on appreciation of the socio-economic and biophysical circumstances, should be used to identify a ‘basket’ of context specific innovations that have potential to address the diverse needs of rural households farming households. [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]

Precision dairy monitoring involves the use of technologies to measure physiological, behavioral, and production indicators on individual animals to detect events of interest. Estrus, disease, and calving detection are common applications, although estrus detection is the most tested and used. Many precision dairy monitoring technologies (PDMT) are commercially available and are being used in research and on farms. As a result, a common question from researchers and producers alike is, "what PDMT should I buy?" The answer to this question is inherently complicated because it depends on many factors, some of which researchers have yet to explore. The objective of this paper is to examine the less quantitively researchable aspects of PDMT adoption and use on-farm. This will be done through 3 lists of 5, determined from published theory and my own experience. First, the 5 main factors that influence adoption of an innovation: (1) relative advantage, (2) compatibility, (3) complexity, (4) trialability, and (5) observability. Each of these factors is at play to a different extent in the 5 adopter categories: (1) innovators, (2) early adopters, (3) early majority, (4) late majority, and (5) laggards. From my experience and research, the top PDMT are those that improve (1) farm efficiency; (2) farm economics; (3) decision-making; (4) animal welfare; and (5) producer happiness. Implementing PDMT on a farm is an enormous and potentially expensive decision. As this part of the industry continues to progress, the potential for different PDMT is endless. Sound research and producer feedback are imperative to ensuring that PDMT continue to improve and become more widely adopted. [ABSTRACT FROM AUTHOR]

Due to its low global share of direct energy consumption and greenhouse gas emissions (1–2%), the implications of technological transitions in the agricultural and forestry sector on the energy system have been overlooked. This paper introduces the Agriculture and Land Use Sector module part of the ModUlar energy System Environment (MUSE), a novel energy system simulation model. The study presents a generalisable method that enables energy modellers to characterise agricultural technologies within an energy system modelling framework. Different mechanisation processes were characterised to simulate intensification/extensification transitions in the sector and its wider implications in the energy and land use system aiming at providing reliable non-energy outputs similarly to those found in dedicated land use models. Additionally, a forest growth model has been integrated to explore the role of reforestation alongside decarbonisation measures in the energy system in achieving carbon mitigation pathways. To illustrate the model's capabilities, Brazil is used as case study. Outputs suggest that by 2030 under a 2 °C mitigation scenario, most of Brazil agricultural production would move from 'transitional' to 'modern' practices, improving productivity and reducing deforestation rates, at the expense of higher energy and fertiliser demand. By mid-century Brazil has the potential to liberate around 24.4 Mha of agricultural land, where large-scale reforestation could have the capacity to sequester around 5.6 GtCO 2 , alleviating mitigation efforts in the energy system, especially reducing carbon capture and storage technology investments in the industry and power sector. • A novel energy-oriented agriculture and land use systems model has been developed. • Mechanisation levels are characterised using cluster analysis and linear optimisation. • Role of reforestation alongside measures in the energy sector in Brazil are studied. • By 2050, modern mechanisation could represent 54–63% of agricultural production. • Brazil has the potential to sequester around 5.6 GtCO 2 through reforestation. [ABSTRACT FROM AUTHOR]

Particularly for developing regions like Sub Saharan Africa, energy intensity is a critical policy issue and usually affected by four challenges: substitution between energy and other factors, technological change, changes in energy composition, and changes of economic growth. This paper examines energy intensity, output elasticity, energy-capital-labor substitution possibility, and factors contribution rates. The study applies a Translog production approach with data spanning 1990–2014 and further, applies ridge regression technique as a robust rectification to achieve unbiasedness in the findings. There is expected evidence of inverse relationship: higher energy intensity in lower per capita economy, evidenced by upward energy tariffs with lower energy elasticity. Factors are inelastic and have average elasticity substitution possibility for capital-energy with their inferential rebound effects' challenges. However, other combinations indicate complementarity at the current development stage. We further, observe that if higher energy intensity not managed, would translate into higher CO 2 intensity, because 99% of total energy is sourced from hydrocarbons of 51% and 48% of biomass. Based on the findings, policymakers and implementors are encouraged to integrate and optimize the whole energy system towards improving energy efficiency through increasing renewable energy composition from the current 1% to at least 10% whiles increasing quality-energy from nonrenewable. • Translog production with Ridge Regression analysis on energy intensity. • Higher energy intensity in lower per capita economy, and low energy elasticity. • The presence of energy substitution has implication for rebound effect on efficiency. • 99% of total energy consumption originates from hydrocarbons and biomass. • Energy poverty and inefficiency as a result of low energy technology innovations. [ABSTRACT FROM AUTHOR]

The emerging bioeconomy requires vast amounts of biomass that current value chains cannot provide. Novel biomass value chains therefore need to be developed. To date, only few novel value chains emerged and additional research on their development is much needed. This paper presents the results of an exploratory case-study analysis of a corn stover value-chain development process in two regions: Ontario (Canada) and Flanders (Belgium). Applying an integrated analytical framework and comparing the results with literature, we identified a number of barriers for novel value-chain development and state seven concrete actions that value-chain actors can take to overcome these barriers. These recommendations can be used by policy makers wishing to facilitate the development of novel biomass value chains in their region; by farmers and industrial actors working to establish new avenues to valorize currently underutilized biomass sources and create additional revenues; and by researchers seeking to disseminate and implement their knowledge about novel technologies as well as socio-economic insights. • Involve all stakeholders in advisory committee to ensure a value-chain perspective. • Independent and competent intermediary is needed to guide value-chain development. • Decide common objective first, but recognize individual actors' goals and concerns. • Non-disclosure agreements and time contribute to creation of trust in value chain. • Transparency about research results is crucial for novel value-chain development. [ABSTRACT FROM AUTHOR]

In this article we explore whether and how the dynamics of access shape the scaling of modern agricultural technologies. It is based on the experience of an agricultural research for development (AR4D) project called CASCAPE, which aims to validate and scale agricultural best practices for smallholder farmers in Ethiopia. The socio-political dynamics of external interventions are often taken for granted contextual factors in AR4D projects. By contrast, this article takes this context as the point of departure for its analysis. The aim of this in-depth case study is to unpack the concept of access as condition for scaling of agricultural technologies. We identify and analyse the mechanisms that determine access to the various components of a malt barley technology package which was introduced in two highland communities in southern Ethiopia (and later 'scaled' to a range of other communities). Our research approach is technographic, implying that we consider the technology to contain both material and social components. The findings suggest that social and clan-based exchange mechanisms (such as clan-based loyalty, reciprocity and vertical accountability) are often rendered invisible even though they are of critical importance in governing access to the material and social components of modern agricultural technologies. Ignoring this socio-political context in the malt barley interventions resulted in an unintended scaling effect in terms of widening the social and economic gap between a few better off farmers and a larger group of poor farmers. The paper thus provides evidence that the socio-political dynamics of access to technology can have an important influence on its wide spread application and may complicate efforts to scale the uptake of technology. Paying more attention to such processes would help to improve the effectiveness of AR4D efforts. • Scaling and adoption arise from a web of interactions in farming communities • Ignoring the socio-political context may result in perpetuating the status quo • Tackling social exclusion requires addressing the constrained agency of the poor • Mechanisms that affect access determine the success or failure of scaling [ABSTRACT FROM AUTHOR]

This study used machine learning, particularly k-means clustering, to identify distinct clusters of users and their technological readiness to adopt various precision livestock farming (PLF) technologies based on their responses to a carefully designed questionnaire. The analysis revealed initially two as well as three distinct clusters representing different levels of technological readiness among farmers considering the adoption of various PLF technologies. In addition to the validation of the cluster results by internal metrics, a related principal component analysis, and a focus group evaluation, this paper describes the application of a Decision Tree as an explainable supervised machine learning approach to investigate the predictive power of specific survey questions. In combination, this research aims to provide valuable insights for understanding farmers' technological readiness, to enhance further survey designs, and to support the development of targeted strategies to promote the successful adoption of PLF technologies in the agricultural sector generally. • Technological readiness of pig and poultry farms was explored. • Two and three clusters of technological readiness were observed by K-Means. • User characteristics for targeted interventions have been highlighted. • Importance of survey questions for cluster identification was calculated through Entropy. • Results have been validated by internal metrics, a PCA, and a Focus Group. [ABSTRACT FROM AUTHOR]

Fruits and vegetables are among the agricultural products that enjoy a high demand in the market. However, the most critical challenge in the production of fruits and vegetables is disease infections which can lead to economic loss. The current assessment method of disease infection still relies on conventional methods and laboratory analysis. The conventional methods although simple and easy are laborious and have less accuracy while laboratory analysis is costly and time-consuming. Over the last two decades, optical imaging techniques have emerged and are gaining interest in the agricultural and food industries. These techniques offer easy, rapid, accurate, reliable as well as user-friendly sensing tools for the identification of quality and disease infection in agricultural products. The fundamental aspect in the techniques is the interaction of light with the tissue and the manipulation of light when interacting with a surface. The integration of edge computing along with artificial intelligence has also reshaped the technology to another level. The use of other agricultural technologies such as drones or robots enables real-time monitoring and efficient farm management. Thus, this paper reviews recent advances in the application of optical imaging techniques for disease detection in fruits and vegetables. This includes computer vision, multispectral imaging, hyperspectral imaging, biospeckle and thermal imaging. The feasibility of the optical imaging techniques is discussed. Challenges and considerations for future research are also highlighted. This review provides a new insight into the recent application of optical imaging which is not only beneficial for the agricultural and food industries but also to other relevant industries. [ABSTRACT FROM AUTHOR]

The quality and safety traceability in the supply chain of agricultural products can tackle low safety and poor transparency and improve consumer buying experiences. Despite consumer green ambitions, agricultural product quality and safety traceability are still lacking. The commercial development concept of the entire agricultural product industrial chain under "Internet + agriculture" is combined with consumers' green expectations. Using Python programming, the text data from web pages are analyzed to obtain and construct the Latent Dirichlet Allocation (LDA) model. The paper identifies the factors influencing the quality and safety traceability of the agricultural product industrial chain under consumers' green demands, improving the existing traceability model. Finally, the study suggests feasible recommendations, including simplifying product packaging, increasing the number of local dealers and sales in the local market, and timely updating authentic and reliable information in the traceability system. [ABSTRACT FROM AUTHOR]

Reducing agricultural carbon emissions is an essential lever for mitigating climate change and promoting high-quality agricultural development. Environmental regulation in the form of resource agglomeration, represented by the high-standard farmland construction policy, plays a vital role in the emission reduction process. Using the panel data of 31 provinces in China from 2001 to 2019 and taking the high-standard farmland construction policy as a quasi-natural experiment, this paper constructs a continuous differences-in-differences (DID) model to explore the impact and mechanism of environmental regulation in the form of resource agglomeration (RAER) on agricultural carbon emission reduction. The results show that RAER significantly reduces agricultural carbon emissions by an average of 3.9%. This conclusion still holds after robustness tests such as endogenous tests, eliminating policy interference, and sample reprocessing. This abatement utility is mainly realized through the technique effect of promoting green technology innovation and the composition effect of optimizing crop cultivation structure. Based on the resource attribute characteristics, the agricultural carbon emission reduction effects of RAER are, in descending order, capital and facility. Further study found heterogeneity in four aspects of the effect of RAER on agricultural carbon emission reduction: regional, planted crops, carbon emission sources, and carbon emission intensity. Therefore, decision-makers should deepen environmental regulation reform through resource agglomeration, implement adapting to local conditions for the high-standard farmland construction policy, and promote policy innovation and system optimization of agricultural carbon emission reduction. [ABSTRACT FROM AUTHOR]

Transitioning to environmentally friendly economic growth is crucial for achieving sustainable development. Digital inclusive finance (DIF) can promote environmentally friendly economic activities and play a crucial role in fostering green and low-carbon development. In this context, a fundamental research question revolves around the theoretical foundations of how DIF impacts the growth of green total factor productivity (GTFP) and whether this relationship can be empirically verified. This question is particularly relevant for rural areas where financial innovations are necessary for the advancement of businesses and society as a whole. Additionally, expanding DIF can bring benefits to the environment, which is affected by agricultural activities. Therefore, it is important to assess the mechanisms behind the development of DIF and its impact on agricultural productivity growth. This paper presents an analysis of the interaction between DIF and GTFP growth in China's agricultural sector. Panel data from 2012 to 2019 at the province level are used for this analysis. The results reveal that GTFP and DIF demonstrate similar growth patterns, with DIF significantly contributing to GTFP growth. DIF can enhance GTFP by promoting information channels and fostering innovation in green technologies. [ABSTRACT FROM AUTHOR]

In recent years, radio frequency identification (RFID) technology has been widely used in smart agriculture. In order to measure the multi-dimensional environmental information during the planting of agricultural products, this paper proposes an integrated humid temperature phosgene (HTLC) sensor based on passive RFID. The structure is optimized by the HFSS software and the multi-dimensional integrated sensor is prepared by engraving. The passive RFID HTLC multidimensional sensor operates stably at 10–50 %RH, 5–25 ℃, 700–1900 lux and 800–1600 ppm. The frequency shift is 80 MHz at 10–50 %RH and the sensitivity is 2 MHz/%RH. In the temperature range of 5–25 ℃, the amplitude change is 12.58 dB and the sensitivity is 0.63 dB/℃. For 700–1900 lux light, the amplitude variation is 10.03 dB and the sensitivity is 0.02 dB/Lux at room temperature. For CO2 concentrations of 800–1600 ppm, the sensor has an amplitude change of 9.43 dB and a sensitivity of 0.012 dB/ppm. The sensor has the advantages of low cost, fast response, good repeatability and good stability, and can collect and display the parameter information of the crop growth environment in multiple dimensions, making the monitoring and management more accurate and intelligent, and helping to adjust the growth environment of the crop in time, thereby improving the yield of the crop. • The proposed sensor tag does not need silicon chip, which is low cost and easy to popularize. • The proposed sensor can measure four environmental parameters. • The proposed sensor consists of nanocomposites as sensitive materials. [ABSTRACT FROM AUTHOR]

• We appraise evidence on the uptake of decision support tools for site-specific nutrient management (SSNM-DSTs). • SSNM-DSTs still struggle to reach scale, despite evidence of benefits. • Scaling constraints vary by type of tool and user characteristics. • SSNM-DST address in-field heterogeneity, but not socioeconomic heterogeneity of users. • Future work should include institutional, economic and governance aspects of scaling. While many have extolled the potential impacts of digital advisory services for smallholder agriculture, the evidence for sustained uptake of such tools remains limited. This paper utilizes a survey of tool developers and researchers, as well as a systematic meta -analysis of prior studies, to assess the extent and challenges of scaling decision support tools for site-specific soil nutrient management (SSNM-DST) across smallholder farming systems, where "scaling" is defined as a significant increase in tool usage beyond pilot levels. Our evaluation draws on relevant literature, expert opinion and apps available in different repositories. Despite their acclaimed yield benefits, we find that SSNM-DST have struggled to reach scale over the last few decades and, with strong heterogeneity in adoption among intended stakeholders and tools. For example, the log odds of a SSNM-DST reaching 5–10 % of the target farmers compared with reaching none, decreases by ∼200% when a technical problem is stated as a reason for the tools' failure to be used at scale. We find a similar decrease in odds ratios when technical, socioeconomic, policy, and R&D constraints were identified as barriers to scaling by national extension and private systems. Meta-regression analysis indicates that the response ratio of using SSNM-DST over Farmer Fertilizer Practice (FFP) varies by non-tool related covariates, such as initial crop yield potential under FFP, current and past crop types, acidity class of the soil, temperature and rainfall regimes, and the amount of input under FFP. In general, the SSNM-DST have moved one step forward compared with the traditional 'blanket' fertilizer recommendation by accounting for in-field heterogeneities in soil and crop characteristics, while remaining undifferentiated in terms of demographic and socioeconomic heterogeneities among users, which potentially constrains adoption at scale. The SSNM-DSTs possess reasonable applicability and can be labeled 'ready' from purely scientific viewpoints, although their readiness for system-level uptake at scale remains limited, especially where socio-technical and institutional constraints are prevalent. [ABSTRACT FROM AUTHOR]

This paper combines panel data from nationally representative household-level surveys in Nigeria with long-term satellite-based spatial data on temperature and precipitation using geo-referenced information related to households. It aims to quantify the impacts of climate change on agricultural productivity, income shares, crop mix, and input use decisions. We measure climate change in harmful degree days, growing degree days, and changes in precipitation using long-term (30 year) changes in temperature and precipitation anomalies during the crop calendars. We find that, controlling for other factors, a 15% (one standard deviation) increase in change in harmful degree days leads to a decrease in agricultural productivity of 5.22% on average. Similarly, precipitation change has resulted in a significant and negative impact on agricultural productivity. Our results further show that the change in harmful degree days decreases the income share from crops and nonfarm self-employment, while it increases the income share from livestock and wage employment. Examining possible transmission channels for this effect, we find that farmers change their crop mix and input use to respond to climate changes, for instance reducing fertilizer use and seed purchases as a response to increases in extreme heat. Based on our findings, we suggest policy interventions that incentivize adoption of climate-resilient agriculture, such as small-scale irrigation and livelihood diversification. We also propose targeted pro-poor interventions, such as low-cost financing options for improving smallholders' access to climate-proof agricultural inputs and technologies, and policy measures to reduce the inequality of access to livelihood capital such as land and other productive assets. [ABSTRACT FROM AUTHOR]

• High-resolution map illustrates 16.8 TWh/year electricity demand for irrigation, refrigeration, incubation, and crop processing across Sub-Saharan Africa. • Irrigation pumping dominates end uses, with demand concentrated in semi-arid regions with high crop production. • Microgrid system sizing and costs compared for hundreds of off-grid sites across Kenya and Zambia, with and without inclusion of agricultural load. • Microgrid case study shows that serving agricultural end-uses along with community load has minimal impact on cost of energy. The use of advanced energy technologies for agricultural purposes—such as irrigation, refrigeration, crop processing, and egg incubation—has the potential to increase crop yield, reduce vulnerability to changing precipitation patterns, increase shelf life, strengthen income and employment opportunities in rural areas, and reduce emissions by displacing fossil fuel-based technologies. These productive uses of energy (PUE) in remote areas could potentially be powered by microgrids that additionally serve otherwise unelectrified communities, most of which are located in rural Sub-Saharan Africa. In this paper, we use high-resolution geospatial data to estimate the end-use electricity demand for a range of agricultural PUE across Sub-Saharan Africa, and we share these data in an open-access mapping tool. Next, we use REopt®, a techno-economic optimization model of energy systems, to determine the cost and system sizing implications of incorporating agricultural PUE into microgrid designs in Kenya and Zambia. We estimate the upper bound of agricultural PUE demand for irrigation, milling, shelling, refrigeration, and egg incubation across Sub-Saharan to be 16.8 TWh/yr. We find that incorporating local agricultural PUE into microgrid system designs increases the required system sizing while having minimal impact on the levelized cost of energy of these systems. Our analysis is the first to demonstrate the PUE potential in the agricultural sector at a 10x10-kilometer resolution across Sub-Saharan Africa and to show, at scale, how site-specific PUE can impact the cost and sizing of microgrids that are otherwise deployed to serve local household and community load. [ABSTRACT FROM AUTHOR]

• Smart farming platform based on edge and cloud computing paradigm is a future trend in agriculture context. • The QoS-aware preference mechanism can prioritize QoS preferences of agricultural applications in smart farm. • The multi-agent offloading approach based on stochastic game can make intelligent offloading decisions for agricultural robots. Intelligent robotics, as a frontier field of widespread attention, is increasingly applied to realize cyber-physical-social system (CPSS). Taking Agriculture 4.0 as one use case, using various agricultural robot applications can reduce labor costs and improve operation efficiency, but limited by the onboard resources, these mobile robots need to make full use of network and computation resources provided by infrastructures in the smart farm to enhance their capabilities. On the one hand, the heterogeneity of networks brings more options for access selection; however, on the other hand, the mobility of robots and the Quality of Service (QoS) requirements of different agricultural applications pose great challenges to the current offloading work. Therefore in this paper, we introduce a stochastic game framework to address these challenges faced by smart agriculture, and propose a multi-agent task offloading and network selection (MATONS) scheme, which utilizes reinforcement learning to offload tasks from robots to central cloud or edge servers. The robots cooperatively optimize the QoS perceived and the energy usage by adopting a joint Nash equilibrium strategy, which requires the decision of offloading along with the optimal transmission technology and corresponding access nodes, i.e., WiFi or cellular network. Simulation results verify that MATONS has great convergence and stability under different scales of robots in the farm, and can achieve superior performance than mainstream algorithms in various scenarios. [ABSTRACT FROM AUTHOR]

Household rooftop photovoltaic technology not only alleviate the reliance on fossil fuels of electric power industries that benefits to environmental protection, but also to enhance rural households' income and relieve poverty. This paper explores the influence of uncertainty and time preference on rural households' adoption of rooftop photovoltaic technology using field experiment in Shandong, China. The results show that most rural households are inclined to risk aversion and ambiguity aversion, half of those are present-biased households. Risk preference, ambiguity preference and time preference obviously and positively impact the possibility of the adoption of rooftop photovoltaic technology, and time preference plays an essential positive moderating role in the impact of uncertainty preference on technology adoption. This research provides a micro view and policy recommendations for household rooftop photovoltaic technology promotion in rural China and sheds light upon how the government can formulate relevant policies to promote the technology. • Measure the households' uncertainty preference using field experiment. • Uncertainty and time preference impact rural rooftop photovoltaic technology adoption. • Time preference plays an important positive moderating role. • Household income and government subsidies affect household technological adoption. [ABSTRACT FROM AUTHOR]

The cultural differences caused by rice cultivation between the north and south of China provide a unique perspective to study the local effect on corporate pro-environmental behavior. In this paper, we investigate the relationship between the geographical distribution of agricultural planting and corporate pro-environmental behavior. We manually collected the city-level rice planting data and used A-share listed companies' data from 2010 to 2019 in China and found that rice culture in the external environment in which the company is embedded has a positive association with the corporate reactive pro-environmental behavior, while rice culture in the internal values which CEO provided with is positively related to the corporate proactive pro-environmental behavior. Moreover, we find that environmental policy and migration are two moderators through which rice culture affects corporate pro-environmental behavior. This study not only sheds light on the agricultural drivers behind corporate pro-environmental behavior and extends the theoretical understanding of how cultural differences caused by geographical factors within countries influence corporate decisions but also helps firms, managers, and stakeholders effectively conduct self-reflection, management, and supervision on environmental issues through farming cultural dimensions. [ABSTRACT FROM AUTHOR]

This paper explores the role that agriculture can play in the food security agenda. To meet this objective, this study measures the impact of the “Programa de Apoyos Directos para la Creación de Iniciativas Agroalimentarias Rurales” (CRIAR), an agricultural technology adoption program implemented in highly food insecure areas of rural Bolivia. The paper analyzes whether changes in food security are due to improved food availability (production and local sales) and/or greater food access (income and production used for home consumption). To this end, data from a sample of 1287 households-817 beneficiaries and 470 controls – interviewed specifically for this purpose are used. To address self-selection issues, the program’s impact is estimated using an instrumental variable model. The results present evidence that program participation improved food security. Specifically, positive impacts on agricultural productivity, agricultural sales and household income are found. These findings confirm the importance of considering agricultural programs as policy tools to address food insecurity. [ABSTRACT FROM AUTHOR]

Summary It has now become almost a stylized fact that sustained agricultural growth is central to rapid poverty reduction and economic development. Yet, world poverty is largely concentrated in the agrarian societies, which have the potential for agricultural productivity growth. This is particularly true for Sub-Saharan African countries, where the gaps between potential and actual yields remain high. Minimizing this gap through the promotion of modern inputs—such as fertilizer and modern seeds—has been at the core of almost all development strategies in Ethiopia. Among other initiatives, the country has promoted microfinance institutions and member-owned financial cooperatives to alleviate credit constraints of the smallholder farmers. This paper analyzes the impacts of these institutions. Using household survey data and a propensity-score-matching technique, we examine the effects that institutional financial services have on farmers’ adoption of agricultural technology in Ethiopia. The results suggest that access to institutional finance has a significant positive impact on both the adoption and extent of technology use. However, when impacts are disaggregated by type of financial institution and farm size, heterogeneities are observed. In particular, financial cooperatives have a greater impact on technology adoption than microfinance institutions, and the results appear to vary depending on farm size and types of inputs. The paper concludes with implications for policies to promote adoption of modern agricultural inputs. [ABSTRACT FROM AUTHOR]

It is still an open question how to assess the contribution of digitalisation in agriculture to the United Nations' Sustainable Development Goals, and how digitalisation then can be done in a responsible way. A socio-cyber-physical system (SCPS) concept can help this analysis, but little experience exists with its operationalisation and application, and its integration with the Responsible Research and Innovation approach. To address this gap, this paper has a twofold purpose: a) operationalise the SCPS concept within an integrated assessment framework adaptable to multiple levels of analysis, contexts, and purposes (e.g. ex-ante, ongoing, ex post evaluation) to shed light on impacts of digitalisation in relation to SCPS entities, relationships, and activities; b) apply the designed framework in 21 multi-stakeholder platforms (Living Labs), which were established to explore needs and expectations in specific subjects relevant for European agriculture, forestry and rural areas. Impacts were assessed through interviews (158 respondents), focus groups (378 participants), online surveys (273 respondents), and other secondary data. The findings indicate that the SCPS framework enables elucidating relationships between digital and broader sustainable development goals and needs, and can sharpen earlier assessments, going beyond a pessimistic or optimistic dichotomy associated to digitalisation by specifying effects and trade-offs in terms of enabling, disenabling, boosting and depleting impacts of digital agriculture. However, the framework being comprehensive and open to emerging socio-cyber-physical interactions, makes that Livings Labs doing participatory impact assessments struggled with the complexity and multiple dimensions of the topic. The paper provides both conceptual and operational knowledge to set up impact evaluations of responsible digitalisation in agriculture and outline concepts that can help anticipating the consequences and trade-offs. [Display omitted] • The paper outlines concepts for anticipating the consequences and trade-offs of digitalisation. • Application of a socio-cyber-physical framework to assess digitalisation impacts in 21 Living Labs. • Presenting a typology of enabling, disenabling, boosting and depleting impacts of digitalisation. • This framework and typology enable impact evaluations for responsible digitalisation in agriculture. • Responsible digitalisation means working in parallel on design, access, and system complexity. [ABSTRACT FROM AUTHOR]

The agricultural sector is undergoing several transitions through "smart-farming" technologies. To make this innovation responsible, it is critical to support technological innovation at different stages of innovation with customized strategies for the individual technology. What are the critical support functions at different stages of technological innovation in agriculture? Four technologies are analysed: Automated Milking Systems (AMS), a digital fencing system for the virtual herding of goats, a technology for drone-based observation and management in agriculture and forestry, and round baler silage systems. These are analysed as sociotechnical constructions of multiple stakeholders, with heterogeneous pathways to societal acceptance and practical usage on the farm. To provide information about how such technology can be created in a responsible manner, the paper suggests a Balanced Readiness Level assessment (BRLa) to highlight five dimensions for technological maturity: technological, market, regulatory, social acceptance, and organizational maturity. Through this approach, the findings show that each phase of the technological development benefits from specifically targeted support and that support functions should not be underestimated in order to get technology to a higher level of acceptance. Agriculture is facing challenges that demand transition of the sector. Supporting invention to stimulate innovation is important; supporting implementation is also important, but identifying the supporting functions that can have an impact at the societal level is crucial to stimulating transition of the system. This paper clarifies the variations of the supporting functions. [Display omitted] • Exploring critical support functions at different stages of technological innovation in agriculture • Balanced Readiness Level assessment (BRLa) highlight technology, market, regulation, social acceptance, and organization • Technology is socio-technically constructed by multiple stakeholders negotiating technological innovation • Crucial to identify the supporting functions that have an impact to stimulate the system's transition at the macro-level [ABSTRACT FROM AUTHOR]

With increasing population, the demand for agricultural productivity is rising to meet the goal of "Zero Hunger". Consequently, farmers have optimized the agricultural activities in a sustainable way with the modern technologies. This integration has boosted the agriculture production due to high potentiality in assisting the farmers. The impulse towards the technological advancement has revived the traditional agriculture methods and resulted in eco-friendly, sustainable, and efficient farming. This has revolutionized the era of smart farming which primarily alliance with modern technologies like, big data, machine learning, deep learning, swarm intelligence, internet-of-things, block chain, robotics and autonomous system, cloud-fog-edge computing, cyber physical systems, and generative adversarial networks (GAN). To cater the same, a detailed survey on ten hot-spots of smart farming is presented in this paper. The survey covers the technology-wise state-of-the-art methods along with their application domains. Moreover, the publicly available data sets with existing research challenges are investigated. Lastly, the paper concludes with suggestions to the identified problems and possible future research directions. • Current ten hot spots of smart farming BD, ML, DL, SI, IoT, BC, cloud-fog-edge, and GAN. • Covering the technology wise methods and current trends. • The available agricultural data sets, research gap, and existing research challenges. • Identified problems and possible future solutions. [ABSTRACT FROM AUTHOR]

New technological solutions can encourage lower household waste production and higher levels of waste separation. This paper focuses on analyzing the role of different behavioral factors, such as empowerment and pro-environmental behavior (PEB), have on citizens' intention to use a novel household waste management and separation system and how these interact with the financial incentives typically applied in this area, pay-as-you-throw (PAYT) and save-as-you-throw (SAYT). The proposed model was tested in Portugal using the structural equation modeling approach. Survey data from 400 respondents found that empowerment plays a vital role in adopting an innovative waste management system. The research discerns pro-environmental behavior (PEB) both as an antecedent and a moderator between system use and empowerment, system use and behavioral intention, and also between system use and financial incentives. We discovered that for people with low perceived PEB, PAYT actually reduces the use of the new waste management system, while SAYT can increase the use of the system. Furthermore, increasing the empowerment of users in the system can work exceptionally well at encouraging consumers that already have a high level of PEB. The paper concludes with a discussion section about the developed framework's application and implication in the waste management sector. This study is valuable for understanding how citizens will adopt a new waste management system and essential for encouraging citizens to engage in recycling behavior regularly. • Explores citizens' use of a novel household waste management system (WMS). • Pro-environmental behavior was identified as an antecedent and a moderator. • Empowerment boosts system's use for people with high pro-environmental behavior. • Pay-as-you-throw reduces system's use if pro-environmental behavior is low. • Save-as-you-throw increases system's use if pro-environmental behavior is low. [ABSTRACT FROM AUTHOR]

Agricultural extension to improve yields of staple food crops and close the yield gap in Sub-Saharan Africa often entails general recommendations on soil fertility management that are distributed to farmers in a large growing area. Site-specific extension recommendations that are better tailored to the needs of individual farmers and fields, and enabled by digital technologies, could potentially bring about yield and productivity improvements. In this paper, we analyze farmers' preferences for high-input maize production supported by site-specific nutrient management recommendations provided by an ICT-based extension tool that is being developed for extension services in the maize belt of Nigeria. We use a choice experiment to provide ex-ante insights on the adoption potentials of site-specific extension services from the perspective of farmers. We control for attribute non-attendance and account for class as well as scale heterogeneity in preferences using different models, and find robust results. We find that farmers have strong preferences to switch from general to ICT-enabled site-specific soil fertility management recommendations which lend credence to the inclusion of digital technologies in agricultural extension. We find heterogeneity in preferences that is correlated with farmers' resource endowments and access to services. A first group of farmers are strong potential adopters; they are better-off, less sensitive to risk, and are more willing to invest in a high-input maize production system. A second group of farmers are weak potential adopters; they have lower incomes and fewer productive assets, are more sensitive to yield variability, and prefer less capital and labor intensive production techniques. Our empirical findings imply that improving the design of extension tools to enable provision of information on the riskiness of expected outcomes and flexibility in switching between low-risk and high-risk recommendations will help farmers to make better informed decisions, and thereby improve the uptake of extension advice and the efficiency of extension programs. • Choice experiement on farmer preferences for site-specific extension recommendations • High willingness to accept ICT-enabled and site-specific extension recommendations • Observed heterogeneity in farmers' willingness to intensify production systems • Information on riskiness of return crucial for improved design of extension tools [ABSTRACT FROM AUTHOR]

Advanced ceramics have many attractive features such as high stability and wear resistance that find broad applications in various fields, e.g. optics, aerospace, etc. However, the accompanying difficult-to-machine property with complex geometry brings great challenges to the commonly used laser machining and rigid wheel based grinding in industry. To achieve optical surface quality with surface roughness below 10 nm Ra, three promising ultra-precision compliant machining technologies using adaptive elastic tools are presented in this paper, including bonnet polishing, compliant pitch polishing and shape adaptive grinding with fine grain size. A comparative study was conducted by machining three different low thermal expansion ceramics while continuously increasing attack angle, spindle speed and tool offset across rectangular regions. Material removal rate (MRR) and surface roughness (Ra) with respect to different process conditions are compared. With sufficient data, the processing ability using above three compliant machining technologies is summarized based on the MRR-Ra plots for different ceramics. In addition, microscopic observation and X-ray diffraction analysis are conducted to characterize differences in material behavior. [ABSTRACT FROM AUTHOR]

Abstract Historically, the focus of the agricultural industry has been increasing profit through maximizing crop yield. Costs for energy and water are small compared to equipment and personnel, and are thus often overlooked. However, energy costs for irrigation are increasing and could be exacerbated with declining water levels in many Western states. This trend has motivated many farmers to explore sustainable irrigation water and energy management practices. Much of this new focus has been directed towards the adoption of new agricultural technologies with a misplaced assumption that technology alone will inherently bring all the benefits. On one hand, farms are going through a paradigm shift, and are turning into net electricity generators, and on the other, higher penetration of intermittent renewable sources into the electricity grid, require dynamic loads to help the grid balance its intra-hour variability and short duration ramps. The agricultural industry could be restructured to utilize larger amounts of renewable energy such as wind and solar and provide a great deal of flexibility to the grid. As emerging producers of clean energy, farmers are required to learn and speak the complex language of the electricity grid in order to monetize their energy generation while making the renewable electricity grid more resilient and reliable. In this paper, we develop a foundational approach for understanding and connecting three important concepts that can help the agricultural industry during this critical transition period. Those three concepts are: (a) current and future needs of the electricity grid, (b) available electricity market mechanisms through which farms can provide services to the grid, and (c) understanding electricity consuming/generating equipment on farms. Defining these concepts and condensing them into a standardized framework, can remove a significant barrier for enabling farms to provide services to the electricity grid while improving their bottom line. Highlights • Agricultural demand response is an underutilized grid resource. • Farms are emerging as net clean energy generators. • Farms need to interact with the grid on both demand and supply sides. • A farm to grid framework is necessary for future of the electricity grid. • Farm loads, grid needs, and electricity markets are currently not well defined. [ABSTRACT FROM AUTHOR]

Abstract The assessment of grassland grazing strategies and institutional arrangements is essential for ensuring the sustainable development of grassland grazing systems. By employing per-pixel grazing information derived from remote sensing data, this paper presents an agent-based model of grassland grazing (ABMGG) for Zeku, China that was designed as a framework for assessing the effects of different combinations of grazing strategies and institutional arrangements on grassland status. By calibrating the parameter values of the ABMGG to the system status values under a policy that has already been implemented, the ABMGG can help us to understand grassland degradation in response to management interventions for each patch of land. In the Zeku implementation, it was found that although different grazing policy scenarios could not significantly improve or decrease the overall grassland leaf area index, a rotational group grazing scenario with a land market tenure system did produce a smaller number of severely degraded grass patches than other policy scenarios (except regional continuous grazing). This provides a new perspective on the consequences of grassland management practices where past research has concentrated more on overall grassland productivity. The ABMGG can extend the ability of policy assessment tools to a high resolution level with pixel-specific real-time remote sensing data, making the assessment results more accurate and representative. Highlights • Design and build an ABM that accounts for the complexities of the grazing system. • The model was calibrated and validated with data derived from remote sensing. • Found that group rotational with land market reduces land degradation. [ABSTRACT FROM AUTHOR]

Highlights • A Bayesian network model is used to generate scenarios for a new energy technology. • Scenarios consider technical and sociopolitical factors and their interactions. • Application to CCS predicts that improvements in both sets of factors are necessary. Abstract The formulation and use of scenarios is now a fundamental part of national and global efforts to assess and plan for climate change. While scenario development initially focused on the technical dimensions of energy, emissions and climate response, in recent years parallel sets of shared socio-economic pathways have been developed to portray the values, motivations, and sociopolitical and institutional dimensions of these systems. However, integrating the technical and social aspects of evolving energy systems is difficult, with transitions dependent on highly uncertain technological advances, social preferences, political governance, climate urgency, and the interaction of these elements to maintain or overcome systemic inertia. A broad range of interdisciplinary knowledge is needed to structure and evaluate these processes, many of which involve a mix of qualitative and quantitative factors. To structure and facilitate the necessary linkages this paper presents an approach for generating a plausible range of scenarios for an emerging energy technology. The method considers influences among technical and social factors that can encourage or impede necessary improvements in the performance and cost of the technology, as well the processes affecting public acceptance and the establishment of governance structures necessary to support effective planning and implementation. A Bayesian network is used to capture relationships among the technological and socioeconomic factors likely to affect the probability that the technology will achieve significant penetration and adoption. The method is demonstrated for carbon capture and storage (CCS): a potential technology on the pathway to deep decarbonization. A preliminary set of expert elicitations is conducted to illustrate how relationships between these factors can be estimated. This establishes a prior or baseline network that can be subsequently analyzed by choosing either optimistic or pessimistic assumptions for respective groups of technical and social variables, identifying sets of key factors that limit or encourage successful deployment. [ABSTRACT FROM AUTHOR]

Highlights • We use field experiments in India to understand how farmers process information. • Most farmers simplify judgments by using only a portion of the information set. • Nearly 40% of farmers rely on first impressions to make subsequent judgments. • More than 36% of farmers use only the most recent piece of information. • Farmers who simplify judgments are less likely to cultivate rice hybrids. Abstract Much empirical research that has shown that an individual's decision to adopt a new technology is the result of learning – both in personal experimentation as well as observing the experimentation of others. Yet even casual observation would suggest significant heterogeneity learning processes, manifesting itself in widely varying patterns of adoption over space and time. In this paper we explore this heterogeneity in the context of early adoption of hybrid rice in rural India. Using specially-designed experiments conducted as part of a primary survey in the field, we are able to identify which of four broad learning heuristics most accurately reflects individuals' information processing strategies. Linking these learning heuristics with observed use of rice hybrids, we demonstrate that pure Bayesian learning is well suited for the tinkering and marginal adjustments that would be required to learn about a technology like hybrid rice, but is also more cognitively taxing, requiring a longer memory and more complex updating processes. Consequently, only about 25 percent of the farmers in our sample can be characterized as pure Bayesian learners. Present-biased learning and relying on first impressions will likely hinder adoption of a technology like hybrid rice, even after controlling for access to credit and a rudimentary proxy for intelligence. [ABSTRACT FROM AUTHOR]

Highlights • The maintenance network for agricultural machinery is defined. • A general framework is developed to select the service provider for maintenance network. • The integrated method of balanced score card and analytic network process is employed to analyze criteria. • The results of the case studies prove that the methodology is effective. Abstract Constructing maintenance networks is becoming an urgent requirement for ensuring the continuous reliable operation and fast response of agricultural machinery maintenance in busy farming seasons. According to dynamic characteristics, time variation, and the cooperativity of agricultural machinery cluster working, this paper proposes a service provider selection framework for the layout of dynamic maintenance network nodes. Furthermore, an integrated analytic network process (ANP) and balanced score card (BSC) service provider selection approach is provided to help construct the maintenance network. Concerning four determinants of BSC: service satisfaction, process capability, knowledge assets, and service cost, this method provides qualitative and quantitative indicators, four evaluative dimensions and a three-layer network evaluation system index. Moreover, this method is applied to maintenance services for agricultural machinery clusters working in cross-regional operations in China. The results indicate that process capability is the most important determinant for a service network, and the proposed method contributes to the ability of decision-makers to comprehend the complex relationship among factors during the service provider selection process. Meanwhile, this method improves the reliability of service provider selection during the construction of a maintenance network. [ABSTRACT FROM AUTHOR]

Highlights • Overview of agricultural machine vision system using statistical ML algorithms. • Supervised statistical ML algorithms include naïve Bayes, DA, kNN and SVMs. • Unsupervised ones include K-means clustering, Fuzzy clustering and GMM. • Highlight the limitations of different statistical ML algorithms in agriculture. • Suggest effective statistical ML algorithms in each specific area in agriculture. Abstract With being rapid increasing population in worldwide, the need for satisfactory level of crop production with decreased amount of agricultural lands. Machine vision would ensure the increase of crop production by using an automated, non-destructive and cost-effective technique. In last few years, remarkable results have been achieved in different sectors of agriculture. These achievements are integrated with machine learning techniques on machine vision approach that cope with colour, shape, texture and spectral analysis from the image of objects. Despite having many applications of different machine learning techniques, this review only described the statistical machine learning technologies with machine vision systems in agriculture due to broad area of machine learning applications. Two types of statistical machine learning techniques such as supervised and unsupervised learning have been utilized for agriculture. This paper comprehensively surveyed current application of statistical machine learning techniques in machine vision systems, analyses each technique potential for specific application and represents an overview of instructive examples in different agricultural areas. Suggestions of specific statistical machine learning technique for specific purpose and limitations of each technique are also given. Future trends of statistical machine learning technology applications are discussed. [ABSTRACT FROM AUTHOR]