Search

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]

The failure of the majority of smallholder farmers in Ethiopia to take advantage of agricultural technologies and economic opportunities in the livestock sector remains an unresolved puzzle. This paper assesses the role of economic, social, political and organizational processes on technology adoption in smallholder livestock production systems based on innovation systems perspective. Functions of the innovation systems framework was used to assess the missing links in the dairy sector value chains. The results show that weak or missing innovation systems functions such as entrepreneurship, knowledge diffusion, market development, and policy advocacy mirror the low uptake of improved technologies in the livestock sector. Shortage of domestic suppliers of key technological inputs and services, weak input and output markets and weak interaction among value chain actors have been the major hindrances to livestock development. For example, there were no reliable sources of improved breeds of dairy heifers, veterinary supplies, quality semen, and equipment. The results suggest that technical knowledge and availability of economic opportunities alone may not be enough to stimulate the uptake of technologies and commercialization of dairy production. The peculiar nature of inputs, services, and output in the livestock sector requires innovative business models that can facilitate the coordination of knowledge, skills, inputs, services and output markets in livestock value chains. • Technology adoption constraints in livestock sector assessed. • Weak entrepreneurship, knowledge diffusion, market development and policy advocacy. • Extension approaches that coordinate knowledge, input and output markets are needed. [ABSTRACT FROM AUTHOR]

A "digital revolution" in agriculture is underway. Advanced technologies like sensors, artificial intelligence, and robotics are increasingly being promoted as a means to increase food production efficiency while minimizing resource use. In the process, agricultural digitalization raises critical social questions about the implications for diverse agricultural labourers and rural spaces as digitalization evolves. In this paper, we use literature and field data to outline some key trends being observed at the nexus of agricultural production, technology, and labour in North America, with a particular focus on the Canadian context. Using the data, we highlight three key tensions observed: rising land costs and automation; the development of a high-skill/low-skilled bifurcated labour market; and issues around the control of digital data. With these tensions in mind, we use a social justice lens to consider the potential implications of digital agricultural technologies for farm labour and rural communities, which directs our attention to racial exploitation in agricultural labour specifically. In exploring these tensions, we argue that policy and research must further examine how to shift the trajectory of digitalization in ways that support food production as well as marginalized agricultural labourers, while pointing to key areas for future research—which is lacking to date. We emphasize that the current enthusiasm for digital agriculture should not blind us to the specific ways that new technologies intensify exploitation and deepen both labour and spatial marginalization. • A "digital revolution" is underway in agriculture marked by technologies like sensors, AI, and robotics. • Digitalization impacts labour and rural communities across industrial agri-food contexts. • New technologies are likely to intensify exploitation and deepen marginalization for most vulnerable. • Equitable, non-exploitative agtech needs to be enhanced and foregrounded. [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 • Subsidies for technology-driven intensification of agriculture are widely considered. • Negative effects of subsidies to promote biomass production can be mitigated. • An integrated Ecosystem Services Framework is a promising policy option. • Alternatives include rural development approaches for agricultural extensification. Abstract Scenario-based approaches provide decision makers with accessible storylines of potential future changes. The parameterisation of such storylines as input variables for integrated assessment models allows using models as a test bed for assessing the effects of alternative land use policy options in different scenarios. However, the potential of this kind of policy-screening analysis can be further improved by assessing the institutional compatibility of the policy options under review. The aim of this paper is to explore the added value of combining institutions-oriented policy analyses with scenario-modelling approaches for improved assessments of EU land use policy options. We describe an expert-based, stepwise process to combine four scenario storylines and two integrated assessment model approaches (CLIMSAVE & IMAGE-GLOBIO) with a procedure for institutional compatibility assessment. Among the subsidies we assessed were those for technology-driven intensification of agricultural production, which would contribute to decreasing demand for cropland across a range of scenarios. In regionalised policy designs, they also contribute to ecological effectiveness, and higher costs of governing. Subsidies to promote biomass production can have negative effects on ecosystems including land conversion, conversion of grassland into cropland as well as conversion of natural forests into managed forests. These effects can to some extent be mitigated by careful policy design which considers the institutional context and features cross-sectoral coordination. An integrated Ecosystem Services Framework policy could accommodate regionalised policy designs and cross-sectoral coordination, however, it can operate only under specific circumstances and needs particular efforts. Rural development approaches are another alternative which feature expansion of cropland by means of a large-scale, bottom-up transformation based on voluntary changes in behaviour, flexibility, participation, and local and regional collaboration. Apart from a vast number of interdisciplinary lessons learned, we also gained insights from the science-policy interface. A weak EU appeared as a plausible scenario from a scientific perspective, given the current political environment. However, it appreaded to be unacceptable at EU level policy making. We decided to maintain scientific independence and looked at policy options also in the context of a weak EU yielding environmentally beneficial opportunities for regional decision making at the expense of relevance of our scenarios to EU level policy makers. [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 • Current adopters of precision agriculture are divergent from current non-adopters. • First adopters are influenced by economic and informational interventions. • Increasing adoption is constrained by scepticism towards economic returns. • EU agricultural policy does not recognise complexity across domains to enable uptake. Abstract Precision agriculture technologies (PATs) offer an approach to arable systems which both enhance productivity and minimise environmental harm. Despite expected economic gains uptake by farmers has been low. This paper explores the intended adoption of PATs through a survey of 971 farmers growing wheat, potato and cotton in five European countries. We apply a count data modelling framework to accommodate the inherent structural differences between the current adopters and non-adopters of PATs. This is augmented by qualitative analysis of the main thematic reasons for intended uptake. Results indicate non-adopters have more belief in their knowledge of field topology and are generally older than current adopters. Those non-adopters intending to adopt PATs in the future are more favourable to a wider range of incentives than current adopters. Attitudinal differences towards the economic certainty of investment and payback periods also emerge. The results indicate that a gradient of adoption is occurring within European arable farming systems which may lead to inequalities in technology access. Recognition of these differences at policy level could lead to cost-effective interventions which maximise uptake, generate returns to candidate farmers and meet policy desires for sustainable agricultural production in the future. [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]

Abstract Decisions about future land use are complex and involve a wide range of factors. The perceptions, intentions, and interests of the stakeholders involved are usually unpredictable. Different stakeholders manage land by choosing different future options and revealing different expectations. Greater proximity to built-up areas confronts farmers with challenges about future land use and land cover change (LUCC). This study aims to identify how external drivers can affect farmers' future LUCC intentions focusing on conversion of agricultural land to urban development. We explore two scenarios projected for the time horizon of 2025 based on farmers' LUCC intentions: A0 – current social and economic trend; and B0 – increasing demand for urban development. We selected the Torres Vedras municipality (Portugal) as case study, an area predominantly agricultural but with a progressively urban intensification in the past two decades. We conducted interviews to capture the farmers' LUCC intentions and modelled an artificial neural network – a multilayered perception to allocate the potential areas for urban development. Parishes with the highest urban pressure were identified using a cluster analysis. These were compared with areas expected to be urbanized (defined in the master plan). Results suggest an increasing farming intensity in the A0 scenario, and an urban growth increase of more than 40% in the B0 scenario, with negative impacts on farming expansion. The outcomes can be applied to spatial planning instruments in order to assist planners to define land transformation priorities and adjust them to spatial trends. Highlights • This paper identifies agricultural areas under urban pressure based on farmers' LUCC intentions for two scenarios. • The findings reveal the importance of incorporate the farmers' future LUCC into the spatial planning strategies. • We identified policy guidance to decision-makers to monitor LUCC. [ABSTRACT FROM AUTHOR]

Highlights • Major sensor data collected from IoT weather stations show spatiotemporal data patterns. • Transform-based lossy compressions can significantly reduce the volume of agricultural IoT data. • Agricultural IoT data exhibit a strong correlation between energy compaction and data fidelity. • Sensor sampling granularities have a significant impact on the data fidelity. Abstract As the volume of data collected by various IoT sensors used in smart farm applications increases, the storing and processing of big data for agricultural applications become a huge challenge. The insight of this paper is that lossy compression can unleash the power of compression to IoT because, as compared with its counterpart (a lossless one), it can significantly reduce the data volume when the spatiotemporal characteristics of IoT sensor data are properly exploited. However, lossy compression faces the challenge of compressing too much data thus losing data fidelity, which might affect the quality of the data and potential analytics outcomes. To understand the impact of lossy compression on IoT data management and analytics, we evaluated four classification algorithms with reconstructed agricultural sensor data based on various energy concentration. Specifically, we applied three transformation-based lossy compression mechanisms to five real-world weather datasets collected at different sampling granularities from IoT weather stations. Our experimental results indicate that there is a strong positive correlation between the concentrated energy of the transformed coefficients and the compression ratio as well as the data quality. While we observed a general trend where much higher compression ratios can be achieved at the cost of a decrease in quality, we also observed that the impact on the classification accuracy varies among the data sets and algorithms we evaluated. Lastly, we show that the sampling granularity also influences the data fidelity in terms of the prediction performance and compression ratio. [ABSTRACT FROM AUTHOR]

Monitoring system based on internet of things (IoT) for the environment-friendly and energy-efficient greenhouse building. [Display omitted] • Review on environmental monitoring system of agricultural greenhouse building. • Essential parameters analysis including greenhouse building environment, soil, and crop. • Comparison and evaluation on components and technologies of the monitoring system. • Discussion of trends and valuable research directions in monitoring system. As a controllable environment, greenhouse has less resource consumption and emission than field crop production and reduced greenhouse gas emissions from agricultural production. Besides, the greenhouse with an intelligent monitoring system has better energy-saving and emission reduction effects. Simultaneously, the intelligent monitoring system can predict the extreme greenhouse environment in advance, reduce diseases and insect pests, reduce the use of pesticides and fertilizers, and provide high-quality food. Researchers are becoming more and more interested in greenhouse monitoring systems, and how to put them into production correctly and effectively is a major challenge. This paper aims to review the intelligent greenhouse monitoring system systematically, serve the data transmission and server processing subsystems by identifying, listing and further explaining the greenhouse environmental parameters and studying the overall design of the greenhouse monitoring system. According to the characteristics of each component of the system, the paper makes a comparative study and obtains its development trend, summarizes the current popular technology and the future development trend of the intelligent monitoring system, and provides support for the research of greenhouse monitoring system. It was found that multi-parameter monitoring is beneficial to achieve effective greenhouse control, and wireless technology has gradually replaced wired mode for data transmission in the environment both inside and outside the greenhouse. Notably, deep learning, big data, and other advanced technologies used in greenhouse monitoring are considered valuable developments, further refine unmanned greenhouse management, and further improve greenhouse construction's energy utilization. [ABSTRACT FROM AUTHOR]

• This paper reviews the latest research progress of image fusion technology in agriculture. • A variety of image fusion methods are introduced to improve the fusion image quality and monitoring accuracy. • The advantages and limitations of various image fusion techniques in crop and livestock monitoring were discussed. • The challenges and prospects of image fusion technology in agriculture are described. Crop and livestock monitoring are essential for guiding agricultural production and improving agricultural yield by facilitating the early prevention and treatment of diseases, yield prediction, and automatic harvesting. However, traditional monitoring methods have some shortcomings, including by humans and through a single sensor, manual monitoring method is time-consuming and labor-intensive, monitoring methods using a single sensor are inaccurate. Therefore, research on improving the methods of crop and livestock monitoring is essential. Image fusion technology can help provide important information from two or more images that can be used to obtain comprehensive agricultural information. After discussing the advantages and disadvantages of various of image fusion methods in agriculture, this paper reviews the application of image fusion in crop recognition and detection, planting area estimation, plant diseases and pest detection, and livestock health assessment and classification. It also highlights the main challenges to the successful application of image fusion technology to agriculture, and discusses directions of future research and development directions in the area. [ABSTRACT FROM AUTHOR]

There is growing interest and importance for responsible research and innovation (RRI) among academic scholars and policy makers, especially, in relation to emerging technologies in the agricultural context. While the evolution of smart technologies in agriculture has led to an increase of available solutions that can be used by farmers, the RRI process of new farming technology has been minimally detailed in research contributions to date. This paper thus aims to describe the first 3 phases of a design thinking process to aid with the development of an agricultural innovation, namely, a geotag photo application for use on smart devices. The design thinking approach involved engaging with target users, such as farmers, farm advisors and inspectors, alongside research scientists, app developers and the national agricultural governing body in Ireland to commence the app development process. This paper describes methodology used to elicit the first three major phases of the design thinking approach: empathise, define and ideation. In the first phase a stakeholder mapping activity was conducted, as well as 7 focus groups and 10 interviews with users and other key actors regarding the challenges and needs related to using the app. The define phase included a reflection of results from the first phase and subsequent development of user-personas and problem statements to inform the third phase. The third phase, ideation, consisted of four interactive user-centred workshops, focusing on app needs, in which ideas and solutions were developed and prioritised. The design thinking approach supported multiple stakeholders to express and evaluate the benefits and challenges they associated with the initial development phases involved in designing a new geotag photo app. It also revealed that, by including farmers and additional actors in developing new farming technology enables technology developers to harness the full value of multiples types of knowledge and expertise. In conclusion, future research on innovation development should consider that by enabling engagement among a wide variety of actors, such as that offered by the initial stages of design thinking, and attending to a greater diversity of values is essential to the development of a responsible, and responsive, digital tool. This study is the first to methodically document the early stages of developing a geotag smartphone app using a design thinking approach. This paper will therefore benefit other scholars aiming to include farmers, and other agricultural stakeholders to have an input on the agri-tech development decisions that will ultimately impact their farming lives. [Display omitted] • Design Thinking was used to develop an app • Methodological development of the app is presented • Design thinking highlighted disparate needs, motivations, and intentions for the app [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]

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]

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]

With the increase in the number of IoT farming datasets, it has become so difficult to identify the right data for IoT agriculture applications. Therefore, a meaningful structure is needed to well understand, interpret and index IoT farming datasets. This paper proposes a new IoT farming ontology that allows the organization, the understanding, and the classification of IoT agriculture datasets knowledge as well as meta-data storage. For this, we have developed a new IoT agriculture taxonomy that helps to identify an IoT agriculture application based on the combination of various IoT agriculture sensors. The evaluation of the semantic IoT agriculture datasets classification, based on the background knowledge provided by the proposed ontology, was achieved using Machine Learning algorithms, including Logistic Regression, Decision Tree Classifier, K-Neighbors Classifier, Linear Discriminant Analysis, Gaussian NB, SVM, and Random Forest Regressor. The obtained results clearly show the effectiveness of the proposed ontology to classify IoT agriculture datasets with high performances and accuracy (0.98), (0.99) using Decision tree classifier and SVM respectively. [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]

• 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]

Technological advances can significantly transform agrarian rural areas by increasing productivity and efficiency while reducing labour intensive processes. For instance, the usage of Unmanned Aerial Vehicles (UAVs) can offer flexibility collecting real-time information of the crops enabling farmers to take timely decisions. However, little is known about the barriers to the adoption of such technologies by rural farmers in emerging economies like India. Building on an extensive literature review, focussed group discussions, and field visits, the barriers impacting the adoption are identified and classified into technical, social, behavioural, operational, economic, and implementation categories. The relevance of each barrier and its importance is evaluated using a hybrid multi-criteria framework built on the theory of Fuzzy Delphi and Fuzzy Analytical Hierarchy Process to identify the most crucial barriers to the adoption of UAVs to implement precision agriculture in rural India. The paper suggests new avenues for accelerating technology adoption in rural areas of emerging economies. [Display omitted] • Adoption of UAVs to implement precision agriculture (PA) is in a nascent stage. • Comprehensive list of barriers impeding the adopting of UAVs to implement PA is identified. • Relevance and relative hierarchy of the barriers in Indian context is evaluated. • Measures to overcome the identified barriers are discussed. [ABSTRACT FROM AUTHOR]

Agriculture, forestry and other land use are important components of a global strategy to limit climate change. Using the example of EU agriculture, this paper examines the potential contribution of technological (i.e. technical and management-based) greenhouse gas mitigation options to climate targets. The quantitative framework presented allows to analyze disparities between marginal abatement cost curves (MACCs) derived from two different commonly used approaches, and between aggregated and regional MACCs. Results highlight the importance of assessing mitigation of agricultural emissions from a multi-dimensional perspective, considering regional heterogeneity of biophysical and economic circumstances and comprising both carbon-dioxide (CO 2) and non-CO 2 emissions. Regarding the ranking of technologies in terms of mitigation potential and costs, the results underline the need to consider technologies in a combined manner, avoiding the simple aggregation of mitigation potentials by individual measures without taking their interactions into account. Focusing only on standalone MACCs can lead to an overestimation of the mitigation potential. Conversely, measures classified as relatively high cost in standalone and aggregated MACCs should not be discarded, as they can still be cost-effective in some regions. The analysis shows that there is no 'one size fits all' rule that could be followed for identifying technologies that should be implemented in all regions. From a policy perspective the results imply that farmers should be granted some flexibility to adopt a set of cost-effective mitigation options that best fits their circumstances. • This paper explores disparities between technology-specific MACCs in agriculture. • Mitigation technologies need to be considered in a combined and regional manner. • Interactions between measures affect cost-effectiveness of individual technologies. • Standalone approaches may discard options that are cost-effective for some regions. • Region-specific flexibility is required to find the most cost-effective technology mix. [ABSTRACT FROM AUTHOR]

As a direct link between farmers and manufacturers of technologies, the characteristics and activities of input suppliers can be expected to play an important role in the generation and diffusion of innovations in agricultural systems. While the agricultural innovation systems (AIS) literature recognises the importance of input suppliers, there are few studies from the Global South assessing the nature and implications of their activities. The aim of this paper is to improve the understanding of how input suppliers can influence the functioning and development of AIS in the Global South. We first adapt the 'functions of innovation systems' framework to examine the role of these private-sector actors in an AIS, identifying three activity categories, through which input suppliers can influence the AIS: market creation for technological innovations, the creation and dissemination of knowledge, and influence on technology priorities. We then apply the framework to a case study of the small-scale irrigation sector in Kenya. The case study documents the emergence of a new cohort of irrigation-equipment suppliers during the period of strong growth in the market for small-scale irrigation technologies since 2000, and examines how they affect the small-scale irrigation agricultural innovation system (SIAIS). We find that Kenyan irrigation-input suppliers perform important activities and roles in the SIAIS aside input supply, notably provision of advisory services, improvement of the supply chain for irrigation technologies, introduction and adaptations of new types of irrigation equipment, and facilitation of access to farm credit. Irrigation-input suppliers in Kenya thus play an important role in the functionality of the SIAIS, particularly regarding knowledge creation and dissemination. The novelty of the paper lies in its empirical assessment of input suppliers in the small-scale irrigation sector and its application of the functions framework. The paper shows that input suppliers can become effective agents of knowledge diffusion once the market has reached a sufficient size and documents how they contributed to knowledge development as they develop, adapt and test specific irrigation equipment. The paper also emphasises that input suppliers can form a key link between national AIS and foreign companies, as they bring in foreign expertise (know-how) to the market along with agricultural technologies. We therefore suggest that policy interventions in support of smallholder irrigation should seek leverage from the growth and capacities of input suppliers as a complement to public research and extension. [Display omitted] • Input suppliers are important actors in agricultural innovation systems (AIS). • We apply a 'functions' approach to study how input suppliers influence AIS. • We present a case on suppliers of small-scale irrigation technologies in Kenya. • Input suppliers influenced several functions in Kenya's small-scale irrigation AIS. • The 'functions' approach enabled an in-depth analysis of input suppliers in AIS. [ABSTRACT FROM AUTHOR]

• The paper develops a methodology for a balanced readiness level assessment of new agricultural technologies. • Technology readiness level (TRL) are supplemented with market readiness level (MRL), regulatory readiness level (RRL), acceptance level readiness (ARL), and organizational level assessment (ORL) to build the balanced readiness level assessment (BRLa). • The authors explore the applicability of the new methodology on 36 new agricultural technologies and one in-depth case study. • The new methodology includes a tool for illustrating results in the shape of a pentagon. In this paper a methodology for a balanced readiness assessment of novel agricultural technologies is developed and presented. The methodology expand on the well-known Technology Readiness Level (TRL) assessments, with a method for assessing TRL as well as Market Readiness Level (MRL), Regulatory Readiness Level (RRL), Acceptance Readiness Level (ARL), and Organizational Readiness Level (ORL) in concert. In the article the Balanced Readiness Level assessment (BRLa) methodology is employed and illustrated on 1) a set of 36 novel agricultural technologies, and 2) on the development of a technology for virtual agricultural fences. The empirical applications in this article indicates that the BRLa- methodology may serve as a fruitful approach for a compound assessment of emerging technologies. The methdodology is relevant for actors involved in advisory services, funding, investment and technology development. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) has been a major influence in Agriculture since its application to the sector. This paper provides an extensive review of the use of smart technologies in agriculture and elaborates the state-of-the-art technologies for smart agriculture including, Internet of Things, cloud computing, machine learning, and artificial intelligence. The application of smart farming to crop and animal production and post-harvesting is discussed. The impact of climate change on agriculture is also considered. This paper contributes to knowledge by iterating the challenges of smart technology to agriculture while highlighting the issues identified from existing framework of smart agriculture. The authors identify many gaps in existing research affecting the application of IoT in smart farming, and suggest further research to improve the current food production globally, to provide better food management and sustainability measures across the globe. [ABSTRACT FROM AUTHOR]

Public and private research institutions are grappling with the challenges and opportunities of embedding dimensions of responsible innovation within their research and development programs, including those seeking to transform agricultural productivity and sustainability through digital technologies. Central to meeting this challenge is building institutional, organisational and professional capacity for anticipation and reflexiveness within multidisciplinary research communities. Foresighting methodologies provide a means by which this might be usefully and practically enabled, whilst also shedding light on the broader social and ethical implications of alternative agricultural technology development pathways under uncertain environmental and industry futures. This paper presents the results of a participatory foresighting exercise undertaken as part of a large, publicly funded multi-disciplinary research initiative designed to build a common big data infrastructure to harness the benefits of the digital revolution for the Australian agricultural and land sectors. We seek to explore what role digital technology will play in the future of Australian agriculture and to consider the social and ethical implications. We ran a one-day foresighting workshop comprised of four steps – 1) horizon scanning to identify trends 2) selecting two drivers of change 3) producing a matrix to generate scenarios 4) building and refining scenarios. Participants explored different possible futures of farming in Australia, with a focus on scenarios involving socio-technical dimensions of digital agriculture to consider the implications of these futures for research practice and for farming communities. Four scenarios were developed, distinguished by the interplay of two critical but uncertain drivers of change identified by participants, namely: the degree of resource security or insecurity that future agricultural enterprises are likely to experience; and the degree to which farming sectors maintain traditional farm business models and associated value chains or transition to more diverse or innovative business models. The process highlighted the need to increase the capacity and opportunity for more reflexivity in research and development, if positive outcomes were to be achieved. The scenarios we produced provide a catalyst for conversation about the implications of digital technology development in Australia and globally, for industry, policy and research and development. In particular, the scenarios highlight potential changes in farm business models, decision making, and beneficiaries and inequities of new technologies and other components of food value chains. The paper also serves as a guide and prompt for others, by demonstrating one way reflexivity can be achieved in organisations attempting innovation. [Display omitted] • We present findings from a workshop to develop four scenarios of digital agriculture futures in Australia. • The aim of the workshop was to identify trends about digital technologies for different forms of agriculture in Australia. • A crucial issue for digital technology is avoiding unintended adverse effects but foresight and reflexivity can help. • Our contribution is outlining a possible method to improve responsible digital innovation through foresighting. • We aim to start the conversation with researchers, industry and policy about achieving responsible digital innovation. [ABSTRACT FROM AUTHOR]

Highlights • Largest producers of crops include south Asian countries including Pakistan. • There is lack of exploitation of technology for enhancements in agriculture sector. • Diagnosis of crops diseases in rural areas is a challenge. • Intelligent mobile application with easy to use interface has been developed using fuzzy inference system. • Highly promising results leading up to 99% of accurate disease diagnosis have been achieved. Abstract South Asian countries are amongst the largest producers of crops with favourable climate conditions and fertile soil. However, traditional agricultural mechanisms are in place and inadequate effort has been put into exploit the usage of technology. One of the main problems being faced by agriculture sector in Pakistan and other developing countries is that crop diseases are not diagnosed timely and efficiently. Conventional methods for disease diagnosis in crops lead to less accurate and inefficient diagnosis, consequently leading to low productivity. In this paper, an intelligent approach for the diagnosis of crop diseases is proposed which is capable of working over Android mobile devices using fuzzy inference system as the main decision making engine at the backend. The system is capable enough to communicate to the farmers in Pakistan in their local language Urdu and assist them in diagnosing diseases in their crops. Agriculture experts in government sector can get equal benefit from it in diagnosis and prevention of crops diseases. It takes symptoms of the crops as input with a provision of vague input and generates the output in the form of diagnosed disease using its inference engine. The proposed system caters two main crops of Pakistan, cotton and wheat and is capable to diagnose their main diseases. The proposed system has been tested on a pool of 100 real crop problems and its inference engine has shown excellent performance in prediction of the right disease which is up to 99% accurate. [ABSTRACT FROM AUTHOR]

In this paper, we present and apply an approximate hypercube queuing method to analyze an emergency maintenance system within the agricultural stage of Brazil’s sugarcane industry. We incorporate differentiated service rates in the approximate hypercube method to deal with significant travel times as well as partial backup and priority in queue to mimic the dispatch policies of the system. We then applied this method using data from a case study conducted in an existing sugar and alcohol mill. We also investigated an alternative scenario for this system, as proposed by the company‘s managers, whereby the number of servers increases. The results showed that the method is an efficient way to investigate the original configuration as well as an alternative scenario for the case study. Furthermore, the method might be useful in larger applications and/or similar agro-industries. [ABSTRACT FROM AUTHOR]

Setting up a sustainable agricultural vegetative waste-management system is a challenging investment task, particularly when markets for output products of waste-treatment technologies are not well established. We conduct an economic analysis of possible investments in treatment technologies of agricultural vegetative waste, while accounting for fluctuating output prices. Under a risk-neutral approach, we find the range of output-product prices within which each considered technology becomes most profitable, using average final prices as the exclusive factor. Under a risk-averse perspective, we rank the treatment technologies based on their computed certainty-equivalent profits as functions of the coefficient of variation of the technologies’ output prices. We find the ranking of treatment technologies based on average prices to be robust to output-price fluctuations provided that the coefficient of variation of the output prices is below about 0.4, that is, approximately twice as high as that of well-established recycled-material markets such as glass, paper and plastic. We discuss some policy implications that arise from our analysis regarding vegetative waste management and its associated risks. [ABSTRACT FROM AUTHOR]

This paper discusses the current state of research on harvesting technologies of the first farming communities of the central and western Mediterranean area between ca . 6000–5900 cal BC and 4800–4700 cal BC. New data obtained from the analysis of almost 40 sites from the Italian Peninsula is compared with data previously collected from the Iberian Peninsula and southern France. Results indicate the existence of at least two different harvesting traditions, one characterized by curved sickles used for harvesting at a low or middle height; the other characterized by reaping knives with parallel hafted blades, probably mainly used for ear harvesting. Processes of innovation and change have been highlighted, suggesting that harvesting techniques changed and evolved through time. Besides, the mechanism and pace of diffusion of curved sickles have been explored, too. [ABSTRACT FROM AUTHOR]

Increased water demands and drought have resulted in the need to provide data to guide deficit water management decisions in irrigated sugarbeet ( Beta vulgaris L.) production. The objective of this study was to quantify the yield response of sugarbeet to water input and actual crop evapotranspiration (ETa) on a soil type (silt loam) common to sugarbeet production in the Northwest U.S. These relationships are valuable to understanding sugarbeet response over a range of water availability and in developing tools to assess future production under water shortages. This paper consolidates data from three studies consisting of ten site-years from 2009 to 2016. The studies were at the USDA-Agricultural Research Service facility in Kimberly, ID on a Portneuf silt loam soil. Treatments consisted of varying levels of cumulative seasonal Kimberly-Penman ET model estimated crop evapotranspiration (ETc) rates ranging from rain-fed to 125% of ETc. Irrigation methods consisted of surface drip irrigation (3 site-years), linear/pivot overhead sprinkler (6 site-years), and solid-set sprinkler (1 site-year). Irrigation frequency was consistent for all studies with applications occurring 2–3 times per week depending on ETc demand. Estimated recoverable sucrose (ERS) yield and root yield were measured, and soil water contents were measured. Across all site-years, quantitative relationships between both actual crop ET (ETa) and water input, and sugarbeet yield and quality variables were developed. Significant (0.05 probability level) positive linear relationships were found between ETa and sugarbeet ERS and root yields (r 2  = 0.78). Estimated recoverable sucrose and root yields increased at rates of 19.4 kg ha −1  mm −1 ETa and 0.13 Mg ha −1  mm −1 ETa, respectively. When ETa depths of 719 and 729 mm were reached by the crop, root and ERS yields were maximized, respectively. When water input (irrigation + precipitation) depths of 598 and 605 mm were applied root and ERS yields were maximized, respectively. The quantitative relationships between both ETa and water input, and sugarbeet yields can be used to quantify sugarbeet production under deficit irrigation conditions (data derived from pivot/linear, drip, and solid set irrigation types), which may arise due to water shortage scenarios, or when drought occurs in non-irrigated areas. [ABSTRACT FROM AUTHOR]

The agricultural sector in Lao PDR is forecast to move from subsistence rice production to a more modernized and market-oriented sector with greater focus on commercialization of agricultural production. Intensification of agricultural production in the southern and central rice growing regions of Lao PDR is problematic as dryland farmers rely on rainfall and soils are poor, yet rural households have been experiencing rapid change in their farming and livelihood systems. This paper employs Q methodology techniques to explore 35 farmers' viewpoints when contemplating their production goals and potential to adopt technologies to improve productivity. Findings describe the two emerging viewpoints among farmers as ‘labour saving productivity maximization’ and ‘traditional labour productivity using improved techniques’. The two viewpoints describe the different issues currently guiding production decisions. While the Lao Government forecasts substantial increases in rice production in the southern plains, farmers will require specialized and tailored support, accounting for their envisaged livelihood and production goals, to allow the sector transformation that many stakeholders currently envisage. [ABSTRACT FROM AUTHOR]

The terms Internet of Things and Big Data are currently subject to much attention, though the specific impact of these terms in our practical lives are difficult to apprehend. Data-driven approaches do lead to new possibilities, and significant improvements within a broad range of domains can be achieved through a cloud-based infrastructure. In the agricultural sector, data-driven precision agriculture shows great potential in facilitating the increase in food production demanded by the increasing world population. However, the adoption rate of precision agriculture technology has been slow, and information and communications technology needed to promote the implementation of precision agriculture is limited by proprietary integrations and non-standardized data formats and connections. In this paper, an open geospatial data infrastructure is presented, based on standards defined by the Open Geospatial Consortium (OGC). The emphasis in the design was on improved interoperability, with the capability of using sensors, performing cloud processing, carrying out regional statistics, and provide seamless connectivity to machine terminals. The infrastructure was implemented through open source software, and was complemented by open data from governmental offices along with ESA satellite imagery. Four use cases are presented, covering analysis of nearly 50 000 crop fields and providing seamless interaction with an emulated machine terminal. They act to showcase both for how the infrastructure enables modularity and interoperability, and for the new possibilities which arise from this new approach to data within the agricultural domain. [ABSTRACT FROM AUTHOR]

Climate change threatens agricultural production and the food security of developing countries in complex ways that demand environmentally friendly innovations. Climate-smart agriculture provides a response to climate change whilst enhancing livelihood of farmers. Climate-smart agricultural technological innovations at farm level have the potential to address climate-related challenges. However, inadequate adoption of these technologies remains a problem. This paper identifies available climate-smart agricultural technological innovations in South Africa and explores their characteristics and context of use using an exploratory research approach. An overview of climate change risks and variability in South Africa and a framework to classify the technological innovations is established based on a literature review. Interviews with expert stakeholders are used to characterise and collect information on available technologies. Results indicate that Conservation Agriculture, Rainwater Harvesting and Seed Varieties that are Drought Tolerant and Early Maturing may be the most suited technologies for climate-smart agriculture in South Africa, particularly for smallholder farmers. However, high initial investment costs, additional labour requirements and management intensity associated with conservation agriculture and rainwater harvesting may pose problems within the South African context. Drought Tolerant and Early Maturing Seed Varieties were noted as less costly and less management intensive, creating better prospects for adoption. This study serves as an initial assessment through the exploration of the available climate-smart agricultural technologies in South Africa. This is essential given that the agricultural sector is faced with the dilemma of responding to climate change related challenges whilst increasing the productivity of farmers. [ABSTRACT FROM AUTHOR]

To tackle the increasing challenges of agricultural production, the complex agricultural ecosystems need to be better understood. This can happen by means of modern digital technologies that monitor continuously the physical environment, producing large quantities of data in an unprecedented pace. The analysis of this (big) data would enable farmers and companies to extract value from it, improving their productivity. Although big data analysis is leading to advances in various industries, it has not yet been widely applied in agriculture. The objective of this paper is to perform a review on current studies and research works in agriculture which employ the recent practice of big data analysis, in order to solve various relevant problems. Thirty-four different studies are presented, examining the problem they address, the proposed solution, tools, algorithms and data used, nature and dimensions of big data employed, scale of use as well as overall impact. Concluding, our review highlights the large opportunities of big data analysis in agriculture towards smarter farming, showing that the availability of hardware and software, techniques and methods for big data analysis, as well as the increasing openness of big data sources, shall encourage more academic research, public sector initiatives and business ventures in the agricultural sector. This practice is still at an early development stage and many barriers need to be overcome. [ABSTRACT FROM AUTHOR]

Navigation system and its navigation algorithm are the crucial parts for intelligent paddy field weeding robot. The environments of paddy fields are complicated in South China. The colors of weed, duckweed and cyanobacteria, which grow in paddy fields, are very similar with rice seedlings. Moreover, the rice seedlings present various morphological features during the growth progress. Therefore, how to extract the guidance lines for navigation system and weeding robot presents various challenges. In order to deal with the above mentioned problems, a navigation method for weeding robot based on SUSAN (smallest univalue segment assimilating nucleus) corner and improved sequential clustering algorithm is proposed in this paper. Firstly, gray feature in paddy field image is extracted by using the adaptive graying algorithm. Secondly, the SUSAN corners are extracted as characteristic points. Thirdly, the seedling navigation line is detected by applying the improved sequential clustering algorithm and Hough Transform. Finally, the position error and angle error are calculated, and a fuzzy controller is designed to control the robot. Experimental results show desirable performances of the proposed method. The proposed segmentation method is effective in complicated environment. [ABSTRACT FROM AUTHOR]

Drip irrigation emitter clogging is one of the key barriers to the development and application of reclaimed water drip irrigation technology. Reclaimed water typically contains large amounts of bacteria, and their excretions, the sticky extracellular polymeric substances (EPS). Indeed, different amounts and types of microorganisms affect drip irrigation emitter clogging, especially bio-clogging process, via excretive EPS. Therefore, it is important to study the dynamic microbial community structure and its effect on the emitter bio-clogging process. In this paper, a drip irrigation experiment using reclaimed water was carried out. Phospholipid fatty acids (PLFAs) in biofilm (bio-clogging substance) were taken as the biomarker of microbial community inside emitters, and the dynamic variation of microbial community in drip irrigation emitter and its effect on the clogging process were studied. The results showed that the microbial growth of biofilms inside 9 types of drip irrigation emitters could enhance emitter clogging, and the PLFAs showed S-shaped pattern with emitter clogging degrees (R 2 > 0.95, p < 0.01), which was closely associated with the variation of microbial community. There were 3–7 types of PLFAs commonly in biofilms within drip irrigation emitters, among which gram positive bacteria (i15:0, 16:0 and 18:0) were dominant in amounts, with the close contents of 24.4%–34.2%, 24.8%–37.2% and 24.2%–39.0%, respectively, and their total proportion exceeding 76.3%. The Pseudomonas (16:0) was found to be the most critical bacteria to affect emitter clogging as it performed better to decompose and utilize organic matters, and showed the best relation with clogging degrees, along with significant relations with other types of bacteria. Therefore, controlling gram positive bacteria, especially Pseudomonas was the most effective way to relieve emitter clogging. Their variation also changed the microbial community structure, and the diversity index ( H ), evenness index ( J ) and dominance index ( D ) of microbial community in biofilms varied within 1.08–1.53, 0.75–1.11 and 0.65–0.75. The diversity index and dominance index both decreased as the amounts and types of microorganisms increased. The total amount of the gram positive bacteria increased after their decrement, and resulted in the similar variation of the microbial community evenness index. The results of this study will establish a theoretical basis for exploring the effects of microbial community variation on emitter bio-clogging, and provide insight into the emitter clogging mechanisms and possible mitigation strategies. [ABSTRACT FROM AUTHOR]

This paper reviews agro-industrial and environmental applications that are using Internet of Things (IoT). It is motivated by the need to identify application areas, trends, architectures and open challenges in these two fields. The underlying survey was developed following a systematic literature review using academic documents written in English and published in peer-reviewed venues from 2006 to 2016. Selected references were clustered into four application domains corresponding to: monitoring, control, logistics, and prediction. Implementation-specific details from each selected reference were compiled to create usage distributions of sensors, actuators, power sources, edge computing modules, communication technologies, storage solutions, and visualization strategies. Finally, the results from the review were compiled into an IoT architecture that represents a wide range of current solutions in agro-industrial and environmental fields. [ABSTRACT FROM AUTHOR]

In the global practice of sustainable agricultural development, China has put forward a plan for agricultural green development, with strict control from the production side to ensure the safety and health of green products "from farming land to dinner table" through strict control from the production side, and green planting is one of the key technologies. Green planting technology (GPT) is an important initiative in China to achieve sustainable agriculture under the Sustainable Development Goals (SDGs). Farmers' acceptance of this technology not only requires national publicity and promotion, but also the adoption of agricultural entities across the country. In this study, we widely distributed questionnaires to farmers in 26 provinces and cities, and received 970 valid responses. This paper uses structural equation model (SEM), a multivariate statistical analysis method, to analyze the influencing factors of farmers' intention to adopt GPT. These results demonstrated that the farmers first consider their own behavioral competences and personal gains and losses before deciding to adopt GPT, followed by the impact of people closely related to farming, while environmental concerns and environmental values rank last. Agricultural technology extension plays an intermediary role in farmers' behavioral intention, and government support acts as a safeguard. The findings will not only help to grasp the reasons behind ineffective promotion of GPT to improve the dissemination and adoption of GPT, but also enable government agencies and authorities to better understand the views of farmers, and subsequently formulate feasible policies and plans. [Display omitted] • Influencing factors of farmers' intention to adopt GPT in China were studied. • Extending TPB theory by introducing internal variable of perceived risk and external variables of agricultural technology extension and government support. • Farmers first consider personal gains and losses before deciding to adopt GPT. • Agricultural technology extension is an important mediating factor influencing behavioral intention of farmer's GPT adoption. • Government support is an important moderating variable of behavioral intention. [ABSTRACT FROM AUTHOR]

Over the years, as an emerging technology, cold plasma (CP) has been widely used in the preservation of agricultural products. Alicyclobacillus spp. are spore-forming bacteria and difficult to inactivate. They adversely affect the economic value of agricultural products. Nevertheless, there are still few studies related to the inactivation of spores using CP. Herein, the inactivation effect and mechanism of dielectric barrier discharge plasma (DBDP) on Alicyclobacillus contaminans (A. contaminans) spores in phosphate-buffered saline and clarified apple juice were investigated. Plasma treatment at 75 V for 9 min achieved 99% inactivation of spores with the initial inocula of 7.13 and 5.72-log in PBS and apple juice, respectively. And the cell structure of the spores was severely disrupted leading to the leakage of the entocyte. Moreover, the surface properties of the spores were altered, making the adhesion of the spores to the hydrophobic surfaces and the stability of the bacterial suspension decreased leading to more agglomeration of the spores. Further results confirmed the intracellular homeostasis of the spores was also disrupted. Therefore, the inactivation mechanism of DBDP on spores was revealed from different perspectives, and our discoveries promote the theoretical progress in the use of plasma in food sterilization. As an emerging nonthermal preservation technology, cold plasma has been used in food processing. In this study, we confirmed the feasibility of the inactivation of Alicyclobacillus contaminans spores in apple juice by dielectric barrier discharge plasma (DBDP) and investigated the mechanism of plasma inactivation of spores at the molecular biology level. This paper provides preliminary support for the application of low-temperature plasma in food processing such as apple juice. (Created with BioRender) [Display omitted] • Dielectric barrier discharge plasma (DBDP) inactivated 99% A. contaminans spores at 75 V with 9 min in apple juice. • DBDP destroyed the membrane structure and cell homeostasis of A. contaminans spores. • Shedding of exospores, reduced hydrophobicity and damaged inner membrane of spores after DBDP-treated. [ABSTRACT FROM AUTHOR]

Agricultural cooperatives remain a significant component of the food and agriculture industry to help the stakeholders to provide services and have opportunities for themselves. One of the aims of an agricultural cooperative is to answer to the needs within the communities of the farmers. Agricultural cooperatives enable individual farmers to increase productivity and maximise their social welfare. Together the farmer members of an agricultural cooperative can buy input supplies cheaper and sell more of their products in larger markets at higher prices, which is not possible for an individual smallholder farmer otherwise. Some studies have shown that farmers who were members of cooperatives have gained higher revenue for their products and spent less on input. However, organising the hundreds of farmers into smaller groups to perform collective farming and marketing is crucial to strengthening their position in the food and agriculture industry. Thereby, in our work, we consider an agricultural cooperative of smallholder farmers as a multi-agent based coalitional model, where coalitions are formed based on the similarity among the smallholder farmers. In this paper, we propose a model and implement a heuristic-based algorithm to find the disjoint partition of the agents set. We evaluate the model and the algorithm based on the following criteria: (i) individual gain, (ii) runtime analysis, (iii) solution quality, and (iv) scalability. We theoretically prove that our coalitional model of an agricultural cooperative has conciseness, expressiveness and efficiency properties. Experimental results confirm that our algorithm is time efficient and scalable. We show, both empirically and theoretically, that our algorithm generates a solution within a bound of the optimal solution. We also show that our coalition model generates positive revenue for the smallholder farmers and the payoff division rule is individual rational. In addition, we generate a new dataset in the context of an agricultural cooperative to show the effectiveness and efficiency of the proposed coalitional model of the cooperative. • Agent-based coalitional model of agricultural cooperative of smallholder farmers. • The novel algorithm forms coalition based on similarity of smallholder farmers. • Simulations demonstrate significant improvements in social welfare. • Presentation of how the coalition formation technique is applied to a real-world scenario. [ABSTRACT FROM AUTHOR]

Critical infrastructures such as healthcare, intelligent transportation, and home automation adapt billions of smart IoT devices to handle real-time data effectively in the digital era. The latest development in sensory and communication technologies collaborate directly with modern applications to bridge networking services without human intervention. Most information systems enable the extension of digital technology to demonstrate the potential use of next-generation network services and management. However, recent advancements open various opportunities, including repudiation, data tampering, and security threats in digital security and privacy. Thus, this survey article chooses a systematic literature review to present a comprehensive survey on authentication and key management in IoT. To discuss the major issues involved in IoT environments, this survey categorizes the work progress into four modules: 1. Discuss the broad aspects of convergence technologies, including healthcare, smart farming, intelligent transportation, etc., to address challenges such as security and privacy in IoT; 2. Conduct a comprehensive review of state-of-the-art technologies to identify security requirements, services, and challenges associated with modern IoT applications; 3. Present a systematic approach to review various key agreement schemes based on network models and performance analysis to examine potential vulnerabilities. 4. Show a thematic analysis to determine suitable corrective measurements dealing with various security and privacy issues. Finally, this paper rationalizes significant progress, including research challenges and future directions in IoT to promote new insights. [ABSTRACT FROM AUTHOR]

Adoption of any new agriculture or aquaculture technology by intended beneficiary farmers is not a simple process. Typically, new technologies and new activities have been delivered to farmers through a 'technology-push', designed and supported by governments, non-government organisations and donor community, yet rates of adoption are disappointingly low. In this paper we set out to explore barriers and enablers for engagement in a new aquaculture activity, as perceived by the intended farmers. Our example is of 'technology-push' towards seaweed farming in coastal villages in Samoa. To increase the likelihood of greater adoption in the future, we explored perceptions of the intended beneficiaries by conducting 20 focus group discussions with 135 women and men, in 10 villages. Specifically, we explored their perceptions of seaweed as an industry, as well as perceptions of barriers and enablers linked to the key market system components: core production functions (inputs and outputs); supporting functions (such as transport, finance, training); institutional barriers; and personal agency. We found that a set of inputs seen as 'essential' was much broader than inputs suggested in market systems studies. Essential inputs included not only materials, tools, equipment and labour, but also extended to include information, knowledge and skills; farm governance and protection of the farms; personal agency; and environmental context. What emerged from our data is a clear division of what participants saw as solutions that can be resolved by them (such as arranging informal institutions) versus solutions that need to be provided externally (such as tools, skills, financing and markets) for which the Ministry of Agriculture and Fisheries was seen as the key external provider. We suggest that, when introducing new activities or technologies, adequate time and effort needs to be put into (a) finding out what essential inputs for the potential beneficiaries of the activity are; and (b) providing them with such inputs in pre-planned manner. • Participants had a positive image of the seaweed sector, a sector with the potential for growth and profit. • Transport, finance and training were identified as supporting functions for extension of seaweed aquaculture. • Information, knowledge and skills were identified as additional essential inputs. • Gender mainstreaming opportunities identified by women included input into decision making and representation on village committees. • Solutions for seaweed aquaculture activities were segregated between the village level and the responsibility of government or external stakeholders. [ABSTRACT FROM AUTHOR]