Opinion: Smart farming is key to developing sustainable agriculture

Pixel to practice: multi-scale image data for calibrating remote-sensing-based winter wheat monitoring methods

Site-specific crop management in heterogeneous fields has emerged as a promising avenue towards increasing agricultural productivity whilst safeguarding the environment. However, successful implementation is hampered by insufficient availability of accurate spatial information on crop growth, vigor, and health status at large scales. Challenges persist particularly in interpreting remote sensing signals within commercial crop production due to the variability in canopy appearance resulting from diverse factors. Recently, high-resolution imagery captured from unmanned aerial vehicles has shown significant potential for calibrating and validating methods for remote sensing signal interpretation. We present a comprehensive multi-scale image dataset encompassing 35,000 high-resolution aerial RGB images, ground-based imagery, and Sentinel-2 satellite data from nine on-farm wheat fields in Switzerland. We provide geo-referenced orthomosaics, digital elevation models, and shapefiles, enabling detailed analysis of field characteristics across the growing season. In combination with rich meta data such as detailed records of crop husbandry, crop phenology, and yield maps, this data set enables key challenges in remote sensing-based trait estimation and precision agriculture to be addressed.

Biomimetic laser-induced graphene fern leaf and enzymatic biosensor for pesticide spray collection and monitoring

Monitoring of pesticide concentration distribution across farm fields is crucial to ensure precise and efficient application while preventing overuse or untreated areas. Inspired by nature's wettability patterns, we developed a biomimetic fern leaf pesticide collection patch using laser-induced graphene (LIG) alongside an external electrochemical LIG biosensor. This "collect-and-sense" system allows for rapid pesticide spray monitoring in the farm field. The LIG is synthesized and patterned on polyimide through a high-throughput gantry-based CO2 laser process, making it amenable to scalable manufacturing. The resulting LIG-based leaf exhibits a remarkable water collection capacity, harvesting spray mist/fog at a rate approximately 11 times greater than a natural ostrich fern leaf when the collection is normalized to surface area. The developed three-electrode LIG pesticide biosensor, featuring a working electrode functionalized with electrodeposited platinum nanoparticles (PtNPs) and the enzyme glycine oxidase, displayed a linear range of 10-260 μM, a detection limit of 1.15 μM, and a sensitivity of 5.64 nA μM-1 for the widely used herbicide glyphosate. Also, a portable potentiostat with a user-friendly interface was developed for remote operation, achieving an accuracy of up to 97%, when compared to a standard commercial benchtop potentiostat. The LIG "collect-and-sense" system can consistently collect and monitor glyphosate spray after 24-48 hours of spraying, a time that corresponds to the restricted-entry interval required to enter most farm fields after pesticide spraying. Hence, this innovative "collect-and-sense" system not only advances precision agriculture by enabling monitoring and mapping of pesticide distribution but also holds the potential to significantly reduce environmental impact, enhance crop management practices, and contribute to the sustainable and efficient use of agrochemicals in modern agriculture.

Measuring air metagenomic diversity in an agricultural ecosystem

All species shed DNA during life or in death, providing an opportunity to monitor biodiversity via environmental DNA (eDNA). In recent years, combining eDNA, high-throughput sequencing technologies, bioinformatics, and increasingly complete sequence databases has promised a non-invasive and non-destructive environmental monitoring tool. Modern agricultural systems are often large monocultures and so are highly vulnerable to disease outbreaks. Pest and pathogen monitoring in agricultural ecosystems is key for efficient and early disease prevention, lower pesticide use, and better food security. Although the air is rich in biodiversity, it has the lowest DNA concentration of all environmental media and yet is the route for windborne spread of many damaging crop pathogens. Our work suggests that ecosystems can be monitored efficiently using airborne nucleic acid information. Here, we show that the airborne DNA of microbes can be recovered, shotgun sequenced, and taxonomically classified, including down to the species level. We show that by monitoring a field growing key crops we can identify the presence of agriculturally significant pathogens and quantify their changing abundance over a period of 1.5 months, often correlating with weather variables. We add to the evidence that aerial eDNA can be used as a source for biomonitoring in terrestrial ecosystems, specifically highlighting agriculturally relevant species and how pathogen levels correlate with weather conditions. Our ability to detect dynamically changing levels of species and strains highlights the value of airborne eDNA in agriculture, monitoring biodiversity changes, and tracking taxa of interest.

Digital innovations for monitoring sustainability in food systems

Monitoring systems that incentivize, track and verify compliance with social and environmental standards are widespread in food systems. In particular, digital monitoring approaches using remote sensing, machine learning, big data, smartphones, platforms and blockchain are proliferating. The increasing use and availability of these technologies put us at a critical juncture to leverage these innovations for enhanced transparency, fairness and open access, rather than descending into a dystopian landscape of digital surveillance and division perpetuated by a powerful few. Here we discuss opportunities and risks, and highlight research gaps linked to the ongoing digitalization of monitoring approaches.

Positive public attitudes towards agricultural robots

Robot technologies could lead to radical changes in farming. But what does the public know and think about agricultural robots? Recent experience with other agricultural technologies-such as plant genetic engineering-shows that public perceptions can influence the pace and direction of innovation, so understanding perceptions and how they are formed is important. Here, we use representative data from an online survey (n = 2269) to analyze public attitudes towards crop farming robots in Germany-a country where new farming technologies are sometimes seen with skepticism. While less than half of the survey participants are aware of the use of robots in agriculture, general attitudes are mostly positive and the level of interest is high. A framing experiment suggests that the type of information provided influences attitudes. Information about possible environmental benefits increases positive perceptions more than information about possible food security and labor market effects. These insights can help design communication strategies to promote technology acceptance and sustainable innovation in agriculture.

Adoption of ICT4D and its determinants: A systematic review and meta-analysis

Various Digital Agricultural Technologies (DAT) have been developed and implemented around the world. This study aims to estimate the overall adoption rate and identify the determinant factors for a better adoption perspective after decades of innovation and dissemination. A systematic review was conducted on published studies that reported adoption rates and determinant factors using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. We used meta-regression and the partial correlation coefficient to estimate the effect size and establish the correlation between socioeconomic characteristics and the adoption of various technologies reported. Fifty-two studies with 32400 participants met the selection criteria and were included in the study. The results revealed an overall pooled adoption rate of 39 %, with the highest adoption rates in developing countries in Africa and South America. Socioeconomic factors such as age, education, gender, and income were found to be the main determinants and should be considered when designing technology for sustainable adoption. The study also found that young farmers were more susceptible to adoption. Moreover, farmers with higher income levels and educational attainment are more likely to use technology linked to agricultural production, market access, and digital advising, implying that high-income farmers with more education are more tech-savvy. However, this does not exclude low-income and low-educated farmers from adopting the technologies, as many models and strategies with socioeconomic considerations were developed. It is one of the reasons behind the underlying enthusiasm for digital agricultural adoption in low and middle-income countries.

A Longitudinal Analysis of the Impact of Digital Technologies on Sustainable Food Production and Consumption in the European Union

In today's landscape, digital technologies hold immense potential in tackling challenges associated with food sustainability. This study aims to contextualize a broader investigation of food sustainability and digitalization within the agricultural sector. Its objective is to explore the influence of digital technologies on sustainable food production and consumption, particularly examining relationships among digital technologies, municipal waste, agricultural output, nitrogen emissions, methane emissions from agriculture, and Goal 12 Responsible Consumption and Production (SDG12). Through the use of Structural Equation Modeling, the empirical investigation scrutinizes the relationships between digital technology use and critical variables linked to food sustainability in a longitudinal analysis. The results highlight the significant impact of extensive digital technology use on municipal waste, sustainable production, and consumption, indirectly influencing greenhouse gas (GHG) emissions. Empirical research findings reveal a negative influence of digital technologies on responsible consumption and production (path coefficient -0.349, p values < 0.001), suggesting an impact of digital technologies on diminishing sustainability in consumption and production. The relationship between digital technologies and municipal solid waste is also negative (path coefficient -0.360, p values < 0.001), suggesting that the use of digital technologies can contribute to reducing the amount of municipal solid waste. Digitalization has the potential to improve the sustainability of supply chains by reducing resource consumption and greenhouse gas emissions associated with production and distribution operations.

Nitrate prediction in groundwater of data scarce regions: The futuristic fresh-water management outlook

Nitrate contamination in groundwater poses a significant threat to water quality and public health, especially in regions with limited data availability. This study addresses this challenge by employing machine learning (ML) techniques to predict nitrate (NO3--N) concentrations in Mexico's groundwater. Four ML algorithms-Extreme Gradient Boosting (XGB), Boosted Regression Trees (BRT), Random Forest (RF), and Support Vector Machines (SVM)-were executed to model NO3--N concentrations across the country. Despite data limitations, the ML models achieved robust predictive performances. XGB and BRT algorithms demonstrated superior accuracy (0.80 and 0.78, respectively). Notably, this was achieved using ∼10 times less information than previous large-scale assessments. The novelty lies in the first-ever implementation of the 'Support Points-based Split Approach' during data pre-processing. The models considered initially 68 covariates and identified 13-19 significant predictors of NO3--N concentration spanning from climate, geomorphology, soil, hydrogeology, and human factors. Rainfall, elevation, and slope emerged as key predictors. A validation incorporated nationwide waste disposal sites, yielding an encouraging correlation. Spatial risk mapping unveiled significant pollution hotspots across Mexico. Regions with elevated NO3--N concentrations (>10 mg/L) were identified, particularly in the north-central and northeast parts of the country, associated with agricultural and industrial activities. Approximately 21 million people, accounting for 10 % of Mexico's population, are potentially exposed to elevated NO3--N levels in groundwater. Moreover, the NO3--N hotspots align with reported NO3--N health implications such as gastric and colorectal cancer. This study not only demonstrates the potential of ML in data-scarce regions but also offers actionable insights for policy and management strategies. Our research underscores the urgency of implementing sustainable agricultural practices and comprehensive domestic waste management measures to mitigate NO3--N contamination. Moreover, it advocates for the establishment of effective policies based on real-time monitoring and collaboration among stakeholders.

Sustainable SMART fertilizers in agriculture systems: A review on fundamentals to in-field applications

Agriculture will face the issue of ensuring food security for a growing global population without compromising environmental security as demand for the world's food systems increases in the next decades. To provide enough food and reduce the harmful effects of chemical fertilization and improper disposal or reusing of agricultural wastes on the environment, will be required to apply current technologies in agroecosystems. Combining biotechnology and nanotechnology has the potential to transform agricultural practices and offer answers to both immediate and long-term issues. This review study seeks to identify, categorize, and characterize the so-called smart fertilizers as the future frontier of sustainable agriculture. The conventional fertilizer and smart fertilizers in general are covered in the first section of this review. Another key barrier preventing the widespread use of smart fertilizers in agriculture is the high cost of materials. Nevertheless, smart fertilizers are widely represented on the world market and are actively used in farms that have already switched to sustainable technologies. The advantages and disadvantages of various raw materials used to create smart fertilizers, with a focus on inorganic and organic materials, synthetic and natural polymers, along with their physical and chemical preparation processes, are contrasted in the following sections. The rate and the mechanism of release are covered. The purpose of this study is to provide a deep understanding of the advancements in smart fertilizers during the last ten years. Trends are also recognized and studied to provide insight for upcoming agricultural research projects.

Farmers' Perspectives of the Benefits and Risks in Precision Livestock Farming in the EU Pig and Poultry Sectors

More efficient livestock production systems are necessary, considering that only 41% of global meat demand will be met by 2050. Moreover, the COVID-19 pandemic crisis has clearly illustrated the necessity of building sustainable and stable agri-food systems. Precision Livestock Farming (PLF) offers the continuous capacity of agriculture to contribute to overall human and animal welfare by providing sufficient goods and services through the application of technical innovations like digitalization. However, adopting new technologies is a challenging issue for farmers, extension services, agri-business and policymakers. We present a review of operational concepts and technological solutions in the pig and poultry sectors, as reflected in 41 and 16 European projects from the last decade, respectively. The European trend of increasing broiler-meat production, which is soon to outpace pork, stresses the need for more outstanding research efforts in the poultry industry. We further present a review of farmers' attitudes and obstacles to the acceptance of technological solutions in the pig and poultry sectors using examples and lessons learned from recent European projects. Despite the low resonance at the research level, the investigation of farmers' attitudes and concerns regarding the acceptance of technological solutions in the livestock sector should be incorporated into any technological development.

The q-rung fuzzy LOPCOW-VIKOR model to assess the role of unmanned aerial vehicles for precision agriculture realization in the Agri-Food 4.0 era

Smart agriculture is gaining a lot of attention recently, owing to technological advancement and promotion of sustainable habits. Unmanned aerial vehicles (UAVs) play a crucial role in smart agriculture by aiding in different phases of agriculture. The contribution of UAVs to sustainable and precision agriculture is a critical and challenging issue to be taken into account, particularly for smallholder farmers in order to save time and money, and improve their agricultural skills. Thence, this study targets to propose an integrated group decision-making framework to determine the best agricultural UAV. Previous studies on UAV evaluation, (i) could not model uncertainty effectively, (ii) weights of experts are not methodically determined; (iii) importance of experts and criteria types are not considered during criteria weight calculation, and (iv) personalized ranking of UAVs is lacking along with consideration to dual weight entities. Herein, nine critical selection criteria are identified, drawing upon the relevant literature and experts' opinions, and five extant UAVs are considered for evaluation. To circumvent the gaps, in this work, a new integrated framework is developed considering q-rung orthopair fuzzy numbers (q-ROFNs) for apt UAV selection. Specifically, methodical estimation of experts' weights is achieved by presenting the regret measure. Further, weighted logarithmic percentage change-driven objective weighting (LOPCOW) technique is formulated for criteria weight calculation, and an algorithm for personalized ranking of UAVs is presented with visekriterijumska optimizacija i kompromisno resenje (VIKOR) approach combined with Copeland strategy. The findings show that the foremost criteria in agricultural UAV selection are "camera," "power system," and "radar system," respectively. Further, it is inferred that the most promising UAV is the DJ AGRAS T30. Since the applicability of UAV in agriculture will get inevitable, the developed framework can be an effective decision support system for farmers, managers, policymakers, and other stakeholders.

IoT and Deep Learning-Based Farmer Safety System

Farming is a fundamental factor driving economic development in most regions of the world. As in agricultural activity, labor has always been hazardous and can result in injury or even death. This perception encourages farmers to use proper tools, receive training, and work in a safe environment. With the wearable device as an Internet of Things (IoT) subsystem, the device can read sensor data as well as compute and send information. We investigated the validation and simulation dataset to determine whether accidents occurred with farmers by applying the Hierarchical Temporal Memory (HTM) classifier with each dataset input from the quaternion feature that represents 3D rotation. The performance metrics analysis showed a significant 88.00% accuracy, precision of 0.99, recall of 0.04, F_Score of 0.09, average Mean Square Error (MSE) of 5.10, Mean Absolute Error (MAE) of 0.19, and a Root Mean Squared Error (RMSE) of 1.51 for the validation dataset, 54.00% accuracy, precision of 0.97, recall of 0.50, F_Score of 0.66, MSE = 0.06, MAE = 3.24, and = 1.51 for the Farming-Pack motion capture (mocap) dataset. The computational framework with wearable device technology connected to ubiquitous systems, as well as statistical results, demonstrate that our proposed method is feasible and effective in solving the problem's constraints in a time series dataset that is acceptable and usable in a real rural farming environment for optimal solutions.

Double transplantation as a climate resilient and sustainable resource management strategy for rice production in eastern Uttar Pradesh, north India

-Enhancing the productivity of rainfed crops, especially rice, while coping with climate adversities and saving critical natural resources is essential for ensuring the food and nutrition security of a growing population. With this context, the present study was undertaken to validate promising farm innovation and adaptation practices used by small-medium landholding farmers for rice cultivation in eastern Uttar Pradesh (UP), north India, as well as to examine the sustainability of innovative practices for large-scale adoption. For this, a 3-year study comprising extensive field surveys and experiments was undertaken to compare single transplantation (ST) and double transplantation (DT) in rice along with organic addition (farm-yard manure, FYM) on crop growth, yield, climate resilience, soil quality, and overall sustainability i.e., social (women involvements and labour productivity), environmental (water productivity and nutrient use efficiency), and economic (benefit:cost ratio) dimensions of sustainability. Field experiments were conducted in triplicate using two local rice varieties (MotiNP-360 and Sampurna Kaveri) in two agroclimatic zones, namely the middle Gangetic plains and the Vindhyan zone, in the Mirzapur district of eastern Uttar Pradesh. The DT practices of rice with and without farm yard manure (FYM) (replacing at a dose of 25% NPK) were evaluated over conventional methods of rice cultivation (i.e., ST, as control) and analysis was done periodically. The DT practice improved growth (p < 0.05), percent fertile tiller and grain (p < 0.05), and rice yield (15-20% higher than ST), while also improving soil quality, yield indices, water and labour productivity, and the benefit-cost ratio. The DT practice also resulted in early maturity (10-15 days earlier than ST), created more labour days for women, decreased lodging and pest/disease incidence, as well as a subsequent reduction in the use of synthetic chemical pesticides and associated environmental costs. Importantly, the residual effects of FYM application significantly improved (p < 0.05) the grain yield in subsequent years of cropping. Optimizing DT cultivation practices, preferably with FYM input for various agro-climatic regions, is essential for large-scale sustainable rice production under changing climatic conditions.

Digital Livestock Technologies as boundary objects: Investigating impacts on farm management and animal welfare

Digital Livestock Technologies (DLTs) can assist farmer decision-making and promise benefits to animal health and welfare. However, the extent to which they can help improve animal welfare is unclear. This study explores how DLTs may impact farm management and animal welfare by promoting learning, using the concept of boundary objects. Boundary objects may be interpreted differently by different social worlds but are robust enough to share a common identity across them. They facilitate communication around a common issue, allowing stakeholders to collaborate and co-learn. The type of learning generated may impact management and welfare differently. For example, it may help improve existing strategies (single-loop learning), or initiate reflection on how these strategies were framed initially (double-loop learning). This study focuses on two case studies, during which two DLTs were developed and tested on farms. In-depth, semi-structured interviews were conducted with stakeholders involved in the case studies (n = 31), and the results of a separate survey were used to complement our findings. Findings support the important potential of DLTs to help enhance animal welfare, although the impacts vary between technologies. In both case studies, DLTs facilitated discussions between stakeholders, and whilst both promoted improved management strategies, one also promoted deeper reflection on the importance of animal emotional well-being and on providing opportunities for positive animal welfare. If DLTs are to make significant improvements to animal welfare, greater priority should be given to DLTs that promote a greater understanding of the dimensions of animal welfare and a reframing of values and beliefs with respect to the importance of animals' well-being.

Towards making the fields talks: A real-time cloud enabled IoT crop management platform for smart agriculture

Agriculture is the primary and oldest industry in the world and has been transformed over the centuries from the prehistoric era to the technology-driven 21^st century, where people are always solving complex problems with the aid of technology. With the power of Information and Communication Technologies (ICTs), the world has become a global village, where every digital object that prevails in the world is connected to each other with the Internet of Things (IoT). The fast proliferation of IoT-based technology has revolutionized practically every sector, including agriculture, shifting the industry from statistical to quantitative techniques. Such profound transformations are reshaping traditional agricultural practices and generating new possibilities in the face of various challenges. With the opportunities created, farmers are now able to monitor the condition of crops in real time. With the automated IoT solutions, farmers can automate tasks in the farmland, as these solutions are capable of making precise decisions based on underlying challenges and executing actions to overcome such difficulties, alerting farmers in real-time, eventually leading to increased productivity and higher harvest. In this context, we present a cloud-enabled low-cost sensorized IoT platform for real-time monitoring and automating tasks dealing with a tomato plantation in an indoor environment, highlighting the necessity of smart agriculture. We anticipate that the findings of this study will serve as vital guides in developing and promoting smart agriculture solutions aimed at improving productivity and quality while also enabling the transition to a sustainable environment.

The Impact Mechanism and Spillover Effect of Digital Rural Construction on the Efficiency of Green Transformation for Cultivated Land Use in China

Under the context of digital economy, agricultural production will be promoted by implementing the strategy of digital rural construction and giving full play to the role of digital factor productivity. This study systematically explains the mechanism of how digital rural construction affects the efficiency of green transformation for cultivated land use. The panel data of 30 provinces in China from 2011 to 2020 are analyzed through two-way fixed effect, spatial Dubin model and other methods, so as to better understand the impact of digital rural construction on the efficiency of green transformation for cultivated land use and its spillover effect. It is discovered in the study that digital rural construction is effective in enhancing the efficiency of green transformation for regional cultivated land use, and that this promoting effect stands multiple robustness tests. According to the heterogeneity analysis, the promoting effect of digital rural construction is more significant in the eastern region and among the samples with high green transformation efficiency of cultivated land use. In addition to improving the efficiency of green transformation for cultivated land use in the region, digital rural construction can also produce a positive spatial spillover effect to a significant extent. On this basis, the targeted policy recommendations are made in this paper. The first one is to improve the efficiency of green transformation for cultivated land use by accelerating the process of digital rural construction. The second one is to pay close attention to the differences in the process of digital rural construction. The third one is to better understand the "welfare sharing" characteristics of digital rural construction. The last one is to establish a mechanism of regional cooperation.

Innovative price-setting approaches to high-value products: A pricing method for agribusiness farmers

Despite being determined by global market prices, the majority of Thai farmers have never become innovative price setters. Not many Thai farmers considered a pricing approach that would maximize the value of their agricultural products. To this end, this study provides empirical evidence regarding the impact of marketing-based variables on pricing. This study aims to identify marketing-based determinants involved in innovative, dynamic price settings for value-added agricultural products. We consider two approaches to innovative pricing - segmented (tiered) pricing and peak-load pricing - to see if there is a possibility for such pricing. A sample of 840 agribusiness farmers was collected from different regions of Thailand. Using multigroup structural invariance analysis, the sample was grouped into four types of farmers: rice, sugarcane, maize, and cassava, to see if there were any differences between them in each of the proposed pricing propensities. Our study finds that cassava farmers tend to pay significant attention to market focus, customer and product differentiation, brand orientation, and segment-based mass customization. Other groups of farmers, like rice and sugarcane, tend to set segmented (tiered) pricing as a result of brand orientation and mass customization. As for peak load pricing, market demand and seasonality are significant factors that can be found among four crops. No matter how prices are set on the global market, this study suggests that agribusiness farmers should think about marketing-related factors to stand out from their competitors.

Recommendations for ethical and responsible use of artificial intelligence in digital agriculture

Artificial intelligence (AI) applications are an integral and emerging component of digital agriculture. AI can help ensure sustainable production in agriculture by enhancing agricultural operations and decision-making. Recommendations about soil condition and pesticides or automatic devices for milking and apple picking are examples of AI applications in digital agriculture. Although AI offers many benefits in farming, AI systems may raise ethical issues and risks that should be assessed and proactively managed. Poor design and configuration of intelligent systems may impose harm and unintended consequences on digital agriculture. Invasion of farmers' privacy, damaging animal welfare due to robotic technologies, and lack of accountability for issues resulting from the use of AI tools are only some examples of ethical challenges in digital agriculture. This paper examines the ethical challenges of the use of AI in agriculture in six categories including fairness, transparency, accountability, sustainability, privacy, and robustness. This study further provides recommendations for agriculture technology providers (ATPs) and policymakers on how to proactively mitigate ethical issues that may arise from the use of AI in farming. These recommendations cover a wide range of ethical considerations, such as addressing farmers' privacy concerns, ensuring reliable AI performance, enhancing sustainability in AI systems, and reducing AI bias.

Future agricultural systems and the role of digitalization for achieving sustainability goals. A review

By leveraging a wide range of novel, data-driven technologies for agricultural production and agri-food value chains, digital agriculture presents potential enhancements to sustainability across food systems. Accordingly, digital agriculture has received considerable attention in policy in recent years, with emphasis mostly placed on the potential of digital agriculture to improve efficiency, productivity and food security, and less attention given to how digitalization may impact other principles of sustainable development, such as biodiversity conservation, soil protection, and human health, for example. Here, we review high-level policy and law in the German and European context to highlight a number of important institutional, societal, and legal preconditions for leveraging digital agriculture to achieve diverse sustainability targets. Additionally, we combine foresight analysis with our review to reflect on how future frame conditions influencing agricultural digitalization and sustainability could conceivably arise. The major points are the following: (1) some polices consider the benefits of digital agriculture, although only to a limited extent and mostly in terms of resource use efficiency; (2) law as it applies to digital agriculture is emerging but is highly fragmented; and (3) the adoption of digital agriculture and if it is used to enhance sustainability will be dependent on future data ownership regimes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13593-022-00792-6.

Sociotechnological Sustainability in Pasture Management: Labor Input and Optimization Potential of Smart Tools to Measure Herbage Mass and Quality

Investing labor time in herbage measurements is important for precision pasture management. In this study, the labor input of three smart herbage measurement tools—multispectral imagery linked to an unmanned aerial vehicle (UAV), a semi-automated rising plate meter (RPM), and near-infrared reflectance spectroscopy (NIRS) of cut herbage samples—and of direct observation was modeled based on the REFA work element method. Three to five users were observed during work execution to identify best-practice workflows. Time measurements were conducted using video footage. The resulting standard times of work elements were used to model labor input for herbage measurements in different farm sizes (i.e., milking platforms of 6–100 ha) and subdivisions of a farm’s milking platform (i.e., 4–45 paddocks). Labor time requirement differed between the smart farming tools (0.7–5.9 h) depending on the farm size and milking platform scenario. The labor time requirement increased for all tools with an increase in farm size and was lowest for the RPM. For the UAV tool, it did not increase noticeably when the division of the milking platform changed. Nevertheless, the potential to save time was identified for the UAV and the NIRS. Therefore, the automation of certain steps in the workflows would contribute to sociotechnological sustainable pasture management.

Introducing Three-Dimensional Scanning for Phenotyping of Olive Fruits Based on an Extensive Germplasm Survey

Morphological characterization of olive (Olea europaea L.) varieties to detect desirable traits has been based on the training of expert panels and implementation of laborious multiyear measurements with limitations in accuracy and throughput of measurements. The present study compares two- and three-dimensional imaging systems for phenotyping a large dataset of 50 olive varieties maintained in the National Germplasm Depository of Greece, employing this technology for the first time in olive fruit and endocarps. The olive varieties employed for the present study exhibited high phenotypic variation, particularly for the endocarp shadow area, which ranged from 0.17−3.34 cm2 as evaluated via 2D and 0.32−2.59 cm2 as determined by 3D scanning. We found significant positive correlations (p < 0.001) between the two methods for eight quantitative morphological traits using the Pearson correlation coefficient. The highest correlation between the two methods was detected for the endocarp length (r = 1) and width (r = 1) followed by the fruit length (r = 0.9865), mucro length (r = 0.9631), fruit shadow area (r = 0.9573), fruit width (r = 0.9480), nipple length (r = 0.9441), and endocarp area (r = 0.9184). The present study unraveled novel morphological indicators of olive fruits and endocarps such as volume, total area, up- and down-skin area, and center of gravity using 3D scanning. The highest volume and area regarding both endocarp and fruit were observed for ‘Gaidourelia’. This methodology could be integrated into existing olive breeding programs, especially when the speed of scanning increases. Another potential future application could be assessing olive fruit quality on the trees or in the processing facilities.

A biodegradable chipless sensor for wireless subsoil health monitoring

Precision Agriculture (PA) is an integral component of the contemporary agricultural revolution that focuses on enhancing food productivity in proportion to the increasing global population while minimizing resource waste. While the recent advancements in PA, such as the integration of IoT (Internet of Things) sensors, have significantly improved the surveillance of field conditions to achieve high yields, the presence of batteries and electronic chips makes them expensive and non-biodegradable. To address these limitations, for the first time, we have developed a fully Degradable Intelligent Radio Transmitting Sensor (DIRTS) that allows remote sensing of subsoil volumetric water using drone-assisted wireless monitoring. The device consists of a simple miniaturized resonating antenna encapsulated in a biodegradable polymer material such that the resonant frequency of the device is dependent on the dielectric properties of the soil surrounding the encapsulated structure. The simple structure of DIRTS enables scalable additive manufacturing processes using cost-effective, biodegradable materials to fabricate them in a miniaturized size, thereby facilitating their automated distribution in the soil. As a proof-of-concept, we present the use of DIRTS in lab and field conditions where the sensors demonstrate the capability to detect volumetric water content within the range of 3.7-23.5% with a minimum sensitivity of 9.07 MHz/%. Remote sensing of DIRTS can be achieved from an elevation of 40 cm using drones to provide comparable performance to lab measurements. A systematic biodegradation study reveals that DIRTS can provide stable readings within the expected duration of 1 year with less than 4% change in sensitivity before signs of degradation. DIRTS provides a new steppingstone toward advancing precision agriculture while minimizing the environmental footprint.

Disruptive Technologies in Smart Farming: An Expanded View with Sentiment Analysis

Smart Farming (SF) is an emerging technology in the current agricultural landscape. The aim of Smart Farming is to provide tools for various agricultural and farming operations to improve yield by reducing cost, waste, and required manpower. SF is a data-driven approach that can mitigate losses that occur due to extreme weather conditions and calamities. The influx of data from various sensors, and the introduction of information communication technologies (ICTs) in the field of farming has accelerated the implementation of disruptive technologies (DTs) such as machine learning and big data. Application of these predictive and innovative tools in agriculture is crucial for handling unprecedented conditions such as climate change and the increasing global population. In this study, we review the recent advancements in the field of Smart Farming, which include novel use cases and projects around the globe. An overview of the challenges associated with the adoption of such technologies in their respective regions is also provided. A brief analysis of the general sentiment towards Smart Farming technologies is also performed by manually annotating YouTube comments and making use of the pattern library. Preliminary findings of our study indicate that, though there are several barriers to the implementation of SF tools, further research and innovation can alleviate such risks and ensure sustainability of the food supply. The exploratory sentiment analysis also suggests that most digital users are not well-informed about such technologies.

Prediction of Harvest Time of Tomato Using Mask R-CNN

In recent years, the agricultural field has been confronting difficulties such as the aging of farmers, a shortage of workers, and difficulties for new farmers. Harvesting time prediction has the potential to solve these problems, and is expected to effectively utilize human resources, save labor, and reduce labor costs. To achieve harvesting time prediction, various works are being actively conducted. Methods for harvesting time prediction using meteorological information such as temperature and solar radiation, etc., and methods for harvesting time prediction using neural networks based on color information from fruit bunch images are being investigated. However, the prediction accuracy is still insufficient, and the harvesting time prediction for individual tomato fruits has not been studied. In this study, we propose a novel method to predict the harvesting time for individual tomato fruits. The method uses Mask R-CNN to detect tomato bunches and uses two types of ripeness determination to predict the harvesting time of individual tomato fruits. The experimental results showed that the accuracy of the prediction using the ratio of R values was better for the harvesting time prediction of tomatoes that are close to the harvesting time, and the accuracy of the prediction using the average of the differences between R and G in RGB values was better for the harvesting time prediction of tomatoes that are far from the harvesting time. These results show the effectiveness of the proposed method.

The Digital Transformation of the Agricultural Value Chain: Discourses on Opportunities, Challenges and Controversial Perspectives on Governance Approaches

The extent to which the digitalisation of agriculture will make a significant contribution to solving urgent sustainability challenges will depend on the design of political, legal and economic frameworks. In this context, social discourses play a central role as they not only reflect collective interpretations and systems of meaning but also reproduce power relations in “truth regimes” and prepare policy actions. While a critical scientific debate on unintended side effects of the digital transformation on agriculture has recently emerged, there is little knowledge about the discourse relations beyond academia. This article presents the results of a discourse analysis during a two-day online conference on the digital transformation of the agricultural value chain. We systematically visited and analysed sessions and presentations. The aim was to identify the main themes, concepts and ideas and different perspectives among actors from science and practice. The results show a wide range of perceived opportunities and challenges but also controversies, especially regarding governance issues such as regulation versus nonregulation, centralised versus decentralised data sharing, the appropriate design of data sovereignty models and trust and evolving inequalities. In addition, it became apparent that discourses on digitalisation are largely expert affairs. We discuss and conclude that a sustainability-oriented digital transformation requires a critical perspective, reflexivity and an adaptive governance approach where science–society collaborations play a central role.

Utilization of Pollution Indices, Hyperspectral Reflectance Indices, and Data-Driven Multivariate Modelling to Assess the Bottom Sediment Quality of Lake Qaroun, Egypt

Assessing the environmental hazard of potentially toxic elements in bottom sediments has always been based entirely on ground samples and laboratory tests. This approach is remarkably accurate, but it is slow, expensive, damaging, and spatially constrained, making it unsuitable for monitoring these parameters effectively. The main goal of the present study was to assess the quality of sediment samples collected from Lake Qaroun by using different groups of spectral reflectance indices (SRIs), integrating data-driven (Artificial Neural Networks; ANN) and multivariate analysis such as multiple linear regression (MLR) and partial least square regression (PLSR). Jetty cruises were carried out to collect sediment samples at 22 distinct sites over the entire Lake Qaroun, and subsequently 21 metals were analysed. Potential ecological risk index (RI), organic matter (OM), and pollution load index (PLI) of lake’s bottom sediments were subjected to evaluation. The results demonstrated that PLI showed that roughly 59% of lake sediments are polluted (PLI > 1), especially samples of eastern and southern sides of the lake’s central section, while 41% were unpolluted (PLI < 1), which composed samples of the western and western northern regions. The RI’s findings were that all the examined sediments pose a very high ecological risk (RI > 600). It is obvious that the three band spectral indices are more efficient in quantifying different investigated parameters. The results showed the efficiency of the three tested models to predict OM, PLI, and RI, revealing that the ANN is the best model to predict these parameters. For instance, the determination coefficient values of the ANN model of calibration datasets for predicting OM, PLI, and RI were 0.999, 0.999, and 0.999, while they were 0.960, 0.897, and 0.853, respectively, for the validation dataset. The validation dataset of the PLSR produced R2 values higher than with MLR for predicting PLI and RI. Finally, the study’s main conclusion is that combining ANN, PLSR, and MLR with proximal remote sensing could be a very effective tool for the detection of OM and pollution indices. Based on our findings, we suggest the created models are easy tools for forecasting these measured parameters.

The adoption of precision agriculture enabling technologies in Swiss outdoor vegetable production: a Delphi study

Digital technologies are a promising means to tackle the increasing global challenges (e.g., climate change, water pollution, soil degradation) and revolutionising agricultural production. The current research used a two-stage Delphi study with 34 experts from various domains, including production, advisory and research, to identify the key drivers and barriers, the most promising technologies and possible measures to support technology adoption in Swiss outdoor vegetable production. Combining these experts' views, the method provides realistic scenarios for future development. In Round 1, open-ended questions were used to collect the experts' opinions. These were then transformed into closed-ended questions for Round 2, where controlled feedback was provided to the experts. Twenty-six experts participated in both rounds, resulting in an overall response rate that was comparably high (76%). It was found that economic factors were important drivers and barriers in technology adoption and, consequently, the experts recommended financial measures to support this adoption. The practical relevance of new technologies provided through communication and education holds further potential in terms of their promotion. These findings are valuable beyond the research field. Educators and policy makers can build on the results and optimally align their efforts to target technology adoption and contribute to more sustainable agriculture.

Digital Twin—Cyber Replica of Physical Things: Architecture, Applications and Future Research Directions

The Internet of Things (IoT) connects massive smart devices to collect big data and carry out the monitoring and control of numerous things in cyber-physical systems (CPS). By leveraging machine learning (ML) and deep learning (DL) techniques to analyze the collected data, physical systems can be monitored and controlled effectively. Along with the development of IoT and data analysis technologies, a number of CPS (smart grid, smart transportation, smart manufacturing, smart cities, etc.) adopt IoT and data analysis technologies to improve their performance and operations. Nonetheless, directly manipulating or updating the real system has inherent risks. Thus, creating a digital clone of a real physical system, denoted as a Digital Twin (DT), is a viable strategy. Generally speaking, a DT is a data-driven software and hardware emulation platform, which is a cyber replica of physical systems. Meanwhile, a DT describes a specific physical system and tends to achieve the functions and use cases of physical systems. Since DT is a complex digital system, finding a way to effectively represent a variety of things in timely and efficient manner poses numerous challenges to the networking, computing, and data analytics for IoT. Furthermore, the design of a DT for IoT systems must consider numerous exceptional requirements (e.g., latency, reliability, safety, scalability, security, and privacy). To address such challenges, the thoughtful design of DTs offers opportunities for novel and interdisciplinary research efforts. To address the aforementioned problems and issues, in this paper, we first review the architectures of DTs, data representation, and communication protocols. We then review existing efforts on applying DT into IoT data-driven smart systems, including the smart grid, smart transportation, smart manufacturing, and smart cities. Further, we summarize the existing challenges from CPS, data science, optimization, and security and privacy perspectives. Finally, we outline possible future research directions from the perspectives of performance, new DT-driven services, model and learning, and security and privacy.

An approach for comparing agricultural development to societal visions

There is broad agreement that agriculture has to become more sustainable in order to provide enough affordable, healthy food at minimal environmental and social costs. But what is "more sustainable"? More often than not, different stakeholders have opposing opinions on what a more sustainable future should look like. This normative dimension is rarely explicitly addressed in sustainability assessments. In this study, we present an approach to assess the sustainability of agricultural development that explicitly accounts for the normative dimension by comparing observed development with various societal visions. We illustrate the approach by analyzing farm- and landscape-scale development as well as sustainability outcomes in a Swiss case study landscape. Observed changes were juxtaposed with desired changes by Avenir Suisse, a liberal think tank representing free-market interests; the Swiss Farmers Association, representing a conservative force; and Landwirtschaft mit Zukunft, an exponent of the Swiss agroecological movement. Overall, the observed developments aligned most closely with desired developments of the liberal think-tank (72%). Farmer interviews revealed that in the case study area farms increased in size (+ 57%) and became more specialized and more productive (+ 223%) over the past 20 years. In addition, interpretation of aerial photographs indicated that farming became more rationalized at the landscape level, with increasing field sizes (+ 34%) and removal of solitary field trees (- 18%). The case study example highlights the varying degrees to which current developments in agriculture align with societal visions. By using societal visions as benchmarks to track the progress of agricultural development, while explicitly addressing their narratives and respective systems of values and norms, this approach offers opportunities to inform also the wider public on the extent to which current developments are consistent with different visions. This could help identify mismatches between desired and actual development and pave the way for designing new policies.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13593-021-00739-3.

A Lab-on-a-Chip Based Automatic Platform for Continuous Nitrites Sensing in Aquaculture

In aquaculture, the density of fish stock, use of feeding, and surrounding environmental conditions can easily result in an excessive concentration of harmful compounds that require continuous monitoring. Chemical sensors are available for most of these compounds, however, operative conditions and continuous monitoring in water make the development of sensors suitable for long and unattended deployments difficult. A possible solution is the development of engineered automatic labs where the uptake of sample and the contact with water is reduced and the use of a minimal quantity of reagents enables the implementation of reliable chemical assays. In this paper, a platform for automatic chemical assays is presented. The concept is demonstrated with the detection of nitrites based on the well-known colorimetric Griess reaction. The platform is centered around a lab-on-a-chip where reagents and water samples are mixed. The color of the reaction product is measured with low-cost optoelectronic components. Results show the feasibility of the approach with a minimum detectable concentration of about 0.1 mg/L which is below the tolerance level for aquaculture farms.

Artificial intelligence: A powerful paradigm for scientific research

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

Agriculture value chain sustainability during COVID-19: an emerging economy perspective

Purpose

Agriculture value chains (AVCs) have experienced unprecedented disruption during the COVID-19 pandemic, with lockdowns and stringent social distancing restrictions making buying and selling behaviours complex and uncertain. This study aims provide a theoretical framework describing the stakeholder behaviours that arise in severely disrupted value chains, which give rise to inter-organisational initiatives that impact industry sustainability.

Design/methodology/approach

A mixed-methods approach is adopted, in which uncertainty theory and relational governance theory and structured interviews with 15 AVC stakeholders underpin the initial conceptual model. The framework is empirically validated via partial least squares structural equation modelling using data from an online survey of 185 AVC stakeholders based in India.

Findings

The findings reveal that buyer and supplier uncertainty created by the COVID-19 lockdowns gives rise to behaviours that encourage stakeholders to engage in relational governance initiatives. Progressive farmers and other AVC stakeholders welcome this improved information sharing, which encourages self-reliance that positively impacts agricultural productivity and sustainability.

Practical implications

The new framework offers farmers and other stakeholders in developing nations possibilities to sustain their AVCs even in dire circumstances. In India, this also requires an enabling ecosystem to enhance smallholders' marketing power and help them take advantage of recent agricultural reforms.

Originality/value

Research is scarce into the impact of buyer and seller behaviour during extreme supply chain disruptions. This study applies relational governance and uncertainty theories, leading to a proposed risk aversion theory.

Electrical Modelling of In-Vivo Impedance Spectroscopy of Nicotiana tabacum Plants

Electrical impedance spectroscopy has been suggested as a sensing method for plants. Here, a theoretical approach for electrical conduction via the plant stem is presented and validated, linking its living electrical characteristics to its internal structure. An electrical model for the alternating current conduction and the associated impedance in a live plant stem is presented. The model accounts for biological and geometrical attributes. It uses the electrically prevalent coupled transmission line model approach for a simplified description of the complicated vessel structure. It considers the electrode coupling to the plant stem (either Galvanic or Faradic), and accounts for the different interactions of the setup. Then the model is simplified using the lumped element approach. The model is then validated using a four-point probe impedance spectroscopy method, where the probes are galvanically coupled to the stem of Nicotiana tabacum plants. The electrical impedance data was collected continuously and the results exhibit an excellent fitting to the theoretical model, with a fitting error of less than 1.5% for data collected on various days and plants. A parametric evaluation of the fitting corresponds to the proposed physically based model, therefore providing a baseline for future plant sensor design.

Application Research: Big Data in Food Industry

A huge amount of data is being produced in the food industry, but the application of big data—regulatory, food enterprise, and food-related media data—is still in its infancy. Each data source has the potential to develop the food industry, and big data has broad application prospects in areas like social co-governance, exploit of consumption markets, quantitative production, new dishes, take-out services, precise nutrition and health management. However, there are urgent problems in technology, health and sustainable development that need to be solved to enable the application of big data to the food industry.

A Systematic Survey on the Role of Cloud, Fog, and Edge Computing Combination in Smart Agriculture

Cloud Computing is a well-established paradigm for building service-centric systems. However, ultra-low latency, high bandwidth, security, and real-time analytics are limitations in Cloud Computing when analysing and providing results for a large amount of data. Fog and Edge Computing offer solutions to the limitations of Cloud Computing. The number of agricultural domain applications that use the combination of Cloud, Fog, and Edge is increasing in the last few decades. This article aims to provide a systematic literature review of current works that have been done in Cloud, Fog, and Edge Computing applications in the smart agriculture domain between 2015 and up-to-date. The key objective of this review is to identify all relevant research on new computing paradigms with smart agriculture and propose a new architecture model with the combinations of Cloud–Fog–Edge. Furthermore, it also analyses and examines the agricultural application domains, research approaches, and the application of used combinations. Moreover, this survey discusses the components used in the architecture models and briefly explores the communication protocols used to interact from one layer to another. Finally, the challenges of smart agriculture and future research directions are briefly pointed out in this article.

Metal-organic frameworks for chemical sensing devices

Metal-organic frameworks (MOFs) are exceptionally large surface area materials with organized porous cages that have been investigated for nearly three decades. Due to the flexibility in their design and predisposition toward functionalization, they have shown promise in many areas of application, including chemical sensing. Consequently, they are identified as advanced materials with potential for deployment in analytical devices for chemical and biochemical sensing applications, where high sensitivity is desirable, for example, in environmental monitoring and to advance personal diagnostics. To keep abreast of new research, which signposts the future directions in the development of MOF-based chemical sensors, this review examines studies since 2015 that focus on the applications of MOF films and devices in chemical sensing. Various examples that use MOF films in solid-state sensing applications were drawn from recent studies based on electronic, electrochemical, electromechanical and optical sensing methods. These examples underscore the readiness of MOFs to be integrated in optical and electronic analytical devices. Also, preliminary demonstrations of future sensors are indicated in the performances of MOF-based wearables and smartphone sensors. This review will inspire collaborative efforts between scientists and engineers working within the field of MOFs, leading to greater innovations and accelerating the development of MOF-based analytical devices for chemical and biochemical sensing applications.

Enhancing crop yield by using Rubisco activase to improve photosynthesis under elevated temperatures

With the rapid growth of world population, it is essential to increase agricultural productivity to feed the growing population. Over the past decades, many methods have been used to increase crop yields. Despite the success in boosting the crop yield through these methods, global food production still needs to be increased to be on par with the increasing population and its dynamic consumption patterns. Additionally, given the prevailing environmental conditions pertaining to the global temperature increase, heat stress will likely be a critical factor that negatively affects plant biomass and crop yield. One of the key elements hindering photosynthesis and plant productivity under heat stress is the thermo-sensitivity of the Rubisco activase (RCA), a molecular chaperone that converts Rubisco back to active form after it becomes inactive. It would be an attractive and practical strategy to maintain photosynthetic activity under elevated temperatures by enhancing the thermo-stability of RCA. In this context, this review discusses the need to improve the thermo-tolerance of RCA under current climatic conditions and to further study RCA structure and regulation, and its limitations at elevated temperatures. This review summarizes successful results and provides a perspective on RCA research and its implication in improving crop yield under elevated temperature conditions in the future.

Factors Affecting e-Government Adoption by Dairy Farmers: A Case Study in the North-West of Spain

With the objective of assessing the farmers’ situation regarding the use of the ICT and their relations with the electronic government, a case study consisting in the realization of 34 face-to-face surveys was conducted between February and March 2020 in dairy farms in the region of Galicia (Spain). The sample was selected according to one of the most important online journals in the farming sector at a national level. From the census, we chose those farms considered most representative taking into account the main criteria: the level of PAC (Common Agrarian Politics) subsidies and milk production (litres/cow and year). The results show that the majority of the farmers used the internet, but on many an occasion, they were discontented in relation to the poor connection quality in their farms. In regard to the use of the electronic government for procedures related to their farms, many of them were able to perform them through the government website; however, there were procedures which the users defined as “complex” and which had to be outsourced to authorised entities. The results also show that the farmers do want to employ the e-government, mainly because of the time and cost saving; however, the current web pages do not meet the users’ expectations. Finally, this situation, applied to a region placed among the 10 most productive regions of milk, is comparable to what happens in other regions.

Can we effectively manage parasites, prions, and pathogens in the global feed industry to achieve One Health?

Prions and certain endoparasites, bacteria, and viruses are internationally recognized as types of disease-causing biological agents that can be transmitted from contaminated feed to animals. Historically, foodborne biological hazards such as prions (transmissible spongiform encephalopathy), endoparasites (Trichinella spiralis, Toxoplasma gondii), and pathogenic bacteria (Salmonella spp., Listeria monocytogenes, Escherichia coli O157, Clostridium spp., and Campylobacter spp.) were major food safety concerns from feeding uncooked or improperly heated animal-derived food waste and by-products. However, implementation of validated thermal processing conditions along with verifiable quality control procedures has been effective in enabling safe use of these feed materials in animal diets. More recently, the occurrence of global Porcine Epidemic Diarrhea Virus and African Swine Fever Virus epidemics, dependence on international feed ingredient supply chains, and the discovery that these viruses can survive in some feed ingredient matrices under environmental conditions of trans-oceanic shipments has created an urgent need to develop and implement rigorous biosecurity protocols that prevent and control animal viruses in feed ingredients. Implementation of verifiable risk-based preventive controls, traceability systems from origin to destination, and effective mitigation procedures is essential to minimize these food security, safety, and sustainability threats. Creating a new biosafety and biosecurity framework will enable convergence of the diverging One Health components involving low environmental impact and functional feed ingredients that are perceived as having elevated biosafety risks when used in animal feeds.

Smart Farming Technologies in Arable Farming: Towards a Holistic Assessment of Opportunities and Risks

Agricultural production finds itself in an area of tension. As a critical infrastructure, it has the task of reliably feeding a growing global population and supplying it with energy. However, the negative environmental impacts caused by agriculture, such as the global loss of biodiversity and the emission of greenhouse gases, are to be reduced. The increasing use of digital technologies is often described as a panacea that enables sustainable agriculture. The relevant literature is very dynamic, but the large number of concepts and terminologies used makes it difficult to obtain an overall view. In addition, many contributions focus on presumed or modeled efficiency gains, but this ignores technical and societal prerequisites and barriers. Therefore, the aim of this work was to identify the opportunities and risks of smart farming (SF) for more ecological arable farming. For this purpose, a holistic and environmental view was taken. The potential of SF to aid in the reduction in the environmental impacts of individual agricultural work steps was examined via an analysis of current literature. In addition, rebound effects, acceptance barriers and political omissions were considered as risks that prevent the benefits from being realized. It was shown that SF is able to contribute to a significant reduction in the negative environmental effects of agriculture. In particular, a reduction in fertilizer and pesticide application rates through mapping, sensing and precise application can lead to environmental benefits. However, achieving this requires the minimization of existing risks. For this reason, a proactive role of the state is required, implementing the necessary governance measures.

Climate-Aware and IoT-Enabled Selection of the Most Suitable Stone Fruit Tree Variety

The application of new technologies such as the Internet of Things offers the opportunity to improve current agricultural development, facilitate daily tasks, and turn farms into efficient and sustainable production systems. The use of these new technologies enables the digital transformation process demanded by the sector and provides agricultural collectives with more optimized analysis and prediction tools. Due to climate change, one of the farm industry’s problems is the advance or decay in the cycle of stone fruit trees. The objective is to recommend whether a specific area meets the minimum climatic requirements for planting certain stone fruit trees based on climatic data and bioclimatic indicators. The methodology used implements a large amount of meteorological data to generate information on specific climatic conditions and interactions on crops. In this work, a pilot study has been carried out in the Region of Murcia using an IoT platform. We simulate scenarios for the development of stone fruit varieties better adapted to the environment. Based on the standard, open interfaces, and protocols, the platform integrates heterogeneous information sources and interoperability with other third-party solutions to exchange and exploit such information.

Sustainable Diets for Cardiovascular Disease Prevention and Management

PURPOSE OF REVIEW

Healthy dietary patterns are recommended for prevention of cardiovascular disease, which remains the leading cause of morbidity and mortality globally. In this review, we discuss dietary patterns that are not only optimal for CVD prevention and management but also sustainable in maximizing health, environmental, and economic benefits.

RECENT FINDINGS

The growing literature on sustainable diets in the context of environmental sustainability includes subtopics of climate change, land use, biodiversity loss, freshwater use, and reactive nitrogen emissions. Similarly, economic sustainability, beyond the retail cost of food, extends to healthcare costs and the economic costs of environmental destruction related to current agricultural practices and food choices. Dietary patterns that are high in plant foods and low in animal foods could maximize health, environmental, and economic benefits; however, questions remain about how to best promote these patterns to achieve wider adoption in an environmentally and economically sustainable way.