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As the global population is expected to reach 10 billion by 2050, the agricultural sector faces the challenge of achieving an increase of 60% in food production without using much more land. This paper explores the potential of Artificial Intelligence (AI) to bridge this "land gap" and mitigate the environmental implications of agricultural land use. Typically, the problem with using AI in such agricultural sectors is the need for more specific infrastructure to enable developers to design AI and ML engineers to deploy these AIs. It is, therefore, essential to develop dedicated infrastructures to apply AI models that optimize resource extraction in the agricultural sector. This article presents an infrastructure for the execution and development of AI-based models using open-source technology, and this infrastructure has been optimized and tuned for agricultural environments. By embracing the MLOps culture, the automation of AI model development processes is promoted, ensuring efficient workflows, fostering collaboration among multidisciplinary teams, and promoting the rapid deployment of AI-driven solutions adaptable to changing field conditions. The proposed architecture integrates state-of-the-art tools to cover the entire AI model lifecycle, enabling efficient workflows for data scientists and ML engineers. Considering the nature of the agricultural field, it also supports diverse IoT protocols, ensuring communication between sensors and AI models and running multiple AI models simultaneously, optimizing hardware resource utilization. Surveys specifically designed and conducted for this paper with professionals related to AI show promising results. These findings demonstrate that the proposed architecture helps close the gap between data scientists and ML engineers, easing the collaboration between them and simplifying their work through the whole AI model lifecycle. [ABSTRACT FROM AUTHOR]

The coupling coordination of agricultural technology innovation with food security is of great significance for high-quality agricultural development. By identifying the coupling coordination relationship between the two systems and the influencing factors, this paper aims to promote the virtuous cycle of coordinated development between regional agriculture and technology, as well as accelerate the realisation of high-quality development of Chinese agriculture. Therefore, this paper explores the spatial and temporal coupling characteristics of the two using the entropy value method, coupling coordination degree model, and exploratory spatial data analysis, and it screens for important influencing factors using the grey correlation model. The main results show that ① the coupling coordination relationship between agricultural technology innovation and food security in China is at a dissonant stage, but the value of the coupling coordination degree increases from 0.2076 to 0.3437 during the period of study, and the level of coordination gradually improves. ② The degree of coupling coordination in the provincial space exhibits a distribution pattern of "high in the east and low in the west". The areas of high value are primarily situated in the provinces of Shandong, Jiangsu, and other provinces along the southeastern coast of China, while the areas of low value are mainly located in the provinces of Qinghai, Ningxia, and other provinces in inland northwest China. ③ The Moran's index of provincial coupling coordination is greater than 0, showing a certain positive correlation, and there is a significant pattern of spatial aggregation. ④ The correlation coefficients between the influencing factors and the degree of coupling coordination are all greater than 0.35, indicating a moderate or high correlation, but the significance of technological support capacity and food distribution security increased over time. [ABSTRACT FROM AUTHOR]

This document, titled "Smart Strategies and Technologies for Sustainability and Biodiversity in Herbaceous and Horticultural Crops," discusses the current trends in modern farming systems that focus on improving the sustainability and biodiversity of agroecosystems. The document highlights various innovative agronomical strategies and new technologies that can help farmers reduce chemical inputs, preserve soil and water quality, decrease emissions, prevent pollution, and lower energy demand. The special issue presented in the document includes 12 papers, with 5 focusing on tree crops/orchards and 7 on herbaceous crops. The papers cover topics such as innovative techniques for tree crops, weed control methods, conservation agriculture, organic farming, cover crops, intercropping, and precision farming technologies. The document concludes that these strategies and technologies can effectively enhance sustainability and biodiversity in herbaceous and horticultural crops in different farming scenarios. [Extracted from the article]

The application of intelligent mobile robots in agriculture has emerged as a new research frontier, with the integration of autonomous navigation technology and intelligent agricultural robots being the key to the widespread adoption of smart agricultural machinery. This paper investigates comprehensive coverage path planning for tracked lawnmowers within orchard environments and addresses challenges related to task allocation and battery life. Firstly, in this study, the motion model of the tracked lawnmower was initially simplified based on assumptions about the orchard environment. Force analyses were conducted on each of its motion mechanisms. For the known orchard environment, a grid-based mapping technique was employed to model the orchard environment. Then, in order to improve the algorithm speed and reduce the number of turns during the lawnmower's traversal, the A* search algorithm was enhanced by combining the method of robot cluster traversal in the orchard environment. Finally, the improved method was simulated and verified in the MATLAB platform to investigate the influence of the number of lawnmower clusters on the path planning in the connected and non-connected orchards. Furthermore, two sets of on-site field trials were meticulously designed to validate the reliability, practicality, and efficacy of the simulation experiments. [ABSTRACT FROM AUTHOR]

Avocado is one of the most commercialized and profitable fruits in the international market. Its cultivation and production are centered in countries characterized by tropical and subtropical climatic conditions, many of them with emerging economies. Moreover, the use of technology is key to agricultural production improvement strategies. Using avocado crop data to forecast the potential impacts of biotic and abiotic factors, combined with smart farming technologies, growers can apply measures during a single production phase to reduce the risks caused by pests and weather variations. Therefore, this paper aims to distinguish the most relevant variables related to agroclimatic and phytosanitary events in avocado crops, their incidence on production and risk management, as well as the emerging technologies used for the identification and analysis of pests and diseases in avocados. A scientific literature search was performed, and the first search found 608 studies, and once the screening process was applied, 37 papers were included in this review. In the results, three research questions were answered that described the pests and diseases with high impact on avocado production, along with the data sources and the principal enabling technologies used in the identification of agroclimatic and phytosanitary events in avocados. Some challenges and trends in the parameterization of the technology in field conditions for data collection are also highlighted. [ABSTRACT FROM AUTHOR]

Global agricultural production is facing unprecedented challenges as the environment becomes increasingly polluted. Governments, scientists, companies and farmers are beginning to focus on appropriate environmental remediation and sustainable agricultural technologies and practices. Innovative environmental adaptation/remediation technologies have been developed and validated, including physical/chemical remediation, green sorbents and bioremediation. The development of environmental remediation technologies has provided additional tools and methods for global agri-environment and food security. The aim of this Special Issue is to bring together 21 cutting-edge research papers covering the latest developments in soil conditions, inorganic pollution, organic pollution, remediation technologies and monitoring methods. The four themes of the Special Issue are "Improvement of agricultural soil properties", "Remediation of potentially toxic element pollution", "Remediation of organic pollution" and "Ecosystem and crop assessment". Based on the results of this Special Issue, we find that combining the latest environmental pollution problems with advanced remediation technologies, continuously promoting technological innovation and policy support, and developing integrated new technologies for environmental protection will be future areas of research for sustainable agro-environmental development. [ABSTRACT FROM AUTHOR]

Unmanned farms employ a variety of sensors, automated systems, and data analysis techniques to enable fully automated and intelligent management. This not only heightens agricultural production efficiency but also reduces the costs associated with human resources. As integral components of unmanned farms' automation systems, agricultural UAVs have been widely adopted across various operational stages due to their precision, high efficiency, environmental sustainability, and simplicity of operation. However, present-day technological advancement levels and relevant policy regulations pose significant restrictions on UAVs in terms of payload and endurance, leading to diminished task efficiency when a single UAV is deployed over large areas. Accordingly, this paper aggregates and analyzes research pertaining to UAV swarms from databases such as Google Scholar, ScienceDirect, Scopus, IEEE Xplorer, and Wiley over the past decade. An initial overview presents the current control methods for UAV swarms, incorporating a summary and analysis of the features, merits, and drawbacks of diverse control techniques. Subsequently, drawing from the four main stages of agricultural production (cultivation, planting, management, and harvesting), we evaluate the application of UAV swarms in each stage and provide an overview of the most advanced UAV swarm technologies utilized therein. Finally, we scrutinize and analyze the challenges and concerns associated with UAV swarm applications on unmanned farms and provide forward-looking insights into the future developmental trajectory of UAV swarm technology in unmanned farming, with the objective of bolstering swarm performance, scalability, and adoption rates in such settings. [ABSTRACT FROM AUTHOR]

Agricultural drones (AUAVs) contribute greatly to sustainable agriculture by reducing input use. The literature on this topic is scarce, so there is little information on the adoption of agricultural drones by farmers. The purpose of this paper is to investigate the factors affecting farmers' intention to adopt drones for agricultural tasks. Within the scope of this study, face-to-face surveys with 384 farmers were conducted. The obtained data were analyzed using different statistical, econometric, and decision techniques, including the conditional valuation method, lower payment bound estimation, probit model regression, fuzzy pairwise comparison, and the Vise Kriterijumska Optimizacija I Kompromisno Resenje-multi-criteria optimization and compromise (VIKOR) technique. The results showed that government support had a positive impact on AUAV purchasing decisions. Farmers' primary borrowing channel preference was interest-free loans. The willingness to rent AUAV technology was higher than the willingness to purchase it, with farmers agreeing to pay TRY 287.54 for one hectare. They preferred cooperatives for the provision of rental services. In general, young farmers who were interested in technology and who had a high agricultural income made up the profile of AUAV adoption. The information obtained from this research not only provides new insights for decision-makers regarding the adoption of AUAV technology but also contributes to the preparation of the promotion process for potential market actors. [ABSTRACT FROM AUTHOR]

China is embarking on a new journey to build a comprehensive socialist modern state in the new era. Modernization of agriculture and forestry is the basis of agricultural modernization, but China's traditional agriculture and forestry industry are facing a more serious crisis of independent research and innovation. As the listed agroforestry companies are directly facing the demands of the market, it becomes essential to study the technological innovation of listed agroforestry companies. Therefore, this paper investigates the relationship between R&D innovation, corporate management, supply chain management, growth capacity, debt servicing capacity, and corporate performance of listed agroforestry companies. Based on the annual panel data of agroforestry listed companies in the CSMAR database from 2010–2021, the empirical study was conducted using panel PVAR models, OLS, 2SLS, LIML, and GMM estimation. The findings show that: (1) Granger causes affecting the supply chain management of listed companies in agroforestry are corporate management, debt servicing capacity, and growth capacity. Granger causes affecting the debt servicing capacity of listed companies in the agroforestry industry are R&D innovation, growth capacity, and corporate performance. Among them, there is a causal influence relationship between debt servicing capacity and corporate performance. (2) R&D innovation, corporate management, supply chain management, growth capacity, debt servicing capacity, and corporate performance contribute the most to its own impulse response, with an average contribution of 87.4%, 81.8%, 86.9%, 96.9%, 86.5%, and 94.7%, respectively. Compared to the other variables, the impulse response contribution of debt servicing capacity to corporate performance was the largest. (3) When supply chain management and growth capability play a fully mediating role, there is a significant positive effect of R&D innovation on corporate performance. Finally, we offer some policy recommendations and suggestions to the Chinese government, as well as some suggestions on how Chinese-listed companies in the agroforestry industry can improve their corporate performance. This paper provides a Chinese case study on the corporate performance of listed companies in the global agroforestry industry. [ABSTRACT FROM AUTHOR]

Research into an innovative weed management approach should focus on a long-term ecological approach, taking into account the interaction between weed communities, crops, management actions, and environmental and socio-economic aspects for each specific agroecosystem [[7], [12]]. From 2018 to 2023, I Agronomy i published 323 papers in the areas of weed science and weed management. Other topics, like VRM (3%), BHM (6%), and INM (7%), confirm the current interest within weed science research towards alternative methods within an integrated and sustainable management approach. [Extracted from the article]

Soil-water practice is essential for farm sustainability, thereby establishing the reference level for agricultural policy of the European Union (EU). This paper focuses on the critical gap in the knowledge surrounding comparison of soil-water effects of Good Agricultural and Environmental Conditions (GAEC) and carbon farming. We aim to interrogate the tasks assigned to soil-water standards during the 2005–2020 timeframe and identify soil-water effects under selected soil-water GAEC topics. The farm-level and landscape-scale effects were weighed for each standard. The investigation included an extensive meta-review of documents that featured scientific work on sustainable practice. In each GAEC document, soil-water sustainability was weighed vis-a-vis carbon farming. Our main finding was that the identification of soil-water effects within GAEC was addressed both at farm-enterprise level (E) and landscape scale (L). This identification was very similar among the sampled Member States (Czech Republic, Hungary, Poland, and Slovakia). A small differentiation was detected in how exact the guidance under each standard was in each of these Member States, and hence how the prioritization was scored, ranging from 1, most influential, to 5, least influential. The scores that prevailed were 2.5–5 on the part of the scoring instrument. Carbon farming is a welcome addition to the corpus of good farming practice and is complementary to GAEC. [ABSTRACT FROM AUTHOR]

Rice, the world's most extensively cultivated cereal crop, serves as a staple food and energy source for over half of the global population. A variety of abiotic and biotic factors such as weather conditions, soil quality, temperature, insects, pathogens, and viruses can greatly impact the quantity and quality of rice grains. Studies have established that plant infections have a significant impact on rice crops, resulting in substantial financial losses in agriculture. To accurately diagnose and manage the diseases affecting rice plants, plant pathologists are seeking efficient and reliable methods. Traditional disease detection techniques, employed by farmers, involve time-consuming visual inspections and result in inadequate farming practices. With advancements in agricultural technology, the identification of pathogenic organisms in rice plants has become significantly more manageable through techniques such as machine learning and deep learning, which are receiving significant attention in crop disease research. In this paper, we used the transfer learning approach on 15 pre-trained CNN models for the automatic identification of Rice leave diseases. Results showed that the InceptionV3 model is outperforming with an average accuracy of 99.64% with Precision, Recall, F1-Score, and Specificity as 98.23, 98.21, 98.20, and 99.80, and the AlexNet model resulted in poor performance with average accuracy of 97.35% among others. [ABSTRACT FROM AUTHOR]

In digital farming, the use of technology to increase agricultural production through automated tasks has recently integrated the development of AgBots for more reliable data collection using autonomous navigation. These AgBots are equipped with various sensors such as GNSS, cameras, and LiDAR, but these sensors can be prone to limitations such as low accuracy for under-canopy navigation with GNSS, sensitivity to outdoor lighting and platform vibration with cameras, and LiDAR occlusion issues. In order to address these limitations and ensure robust autonomous navigation, this paper presents a sensor selection methodology based on the identification of environmental conditions using sensor data. Through the extraction of features from GNSS, images, and point clouds, we are able to determine the feasibility of using each sensor and create a selection vector indicating its viability. Our results demonstrate that the proposed methodology effectively selects between the use of cameras or LiDAR within crops and GNSS outside of crops, at least 87% of the time. The main problem found is that, in the transition from inside to outside and from outside to inside the crop, GNSS features take 20 s to adapt. We compare a variety of classification algorithms in terms of performance and computational cost and the results show that our method has higher performance and lower computational cost. Overall, this methodology allows for the low-cost selection of the most suitable sensor for a given agricultural environment. [ABSTRACT FROM AUTHOR]

The growth of the Indonesian population has led to an increase in the demand for rice, which the country has yet to satisfy. Indonesia needs a comprehensive strategy that integrates meaningful efforts to increase its agricultural production. This study aims to review the examined trends in rice yield in Indonesia for 70 years after Indonesia's independence (1945–2016) followed by the identification of the application technology and factors that contribute to increasing rice yields to forecast sustainable food security scenarios up to 2030. This article reviews the results of research on rice production technology in Indonesia from 1945 to 2016, and the outlook for 2030. This paper examines the main points of the Indonesian transformation of rice technology: improvement of rice varieties, integrated crop management, innovations in agricultural machinery, and the Integrated Cropping Calendar Information System (ICCIS). We found that transformation has helped Indonesia increased its rice yields from 3 t ha−1 prior to 1961 to 4.6 t ha−1 in 1985, stagnated in 1990, and increased again in 2017 to 5.46 t ha−1. The increase in yield was sustained by an increase in the harvested area owing to cropping index (CI) innovation. Food security and sustainable development remain the primary goals of Indonesia's agricultural sector. The application of appropriate technologies and institutional innovations can assist Indonesia in achieving its food security. Therefore, the transformation of technological innovations will continue to be an essential driver of future agricultural growth, including greater use of crop varieties, machinery, and land/institutional reforms. [ABSTRACT FROM AUTHOR]

In view of the continuous increase in labor costs for complex picking tasks, there is an urgent demand for intelligent harvesting robots in the global fresh fruit cultivation industry. Fruit visual information is essential to guide robotic harvesting. However, obtaining accurate visual information about the target is critical in complex agricultural environments. The main challenges include the image color distortion under changeable natural light, occlusions from the interlaced plant organs (stems, leaves, and fruits), and the picking point location on fruits with variable shapes and poses. On top of summarizing the current status of typical fresh fruit harvesting robots, this paper outlined the state-of-the-art advance of visual information acquisition technology, including image acquisition in the natural environment, fruit recognition from the complex backgrounds, target stereo locating and measurement, and fruit search among the plants. It then analyzed existing problems and raised future potential research trends from two aspects, multiple images fusion and self-improving algorithm model. [ABSTRACT FROM AUTHOR]

Facing the need of modern agriculture to accurately grasp the information of farmland diseases and pests, this paper proposes an intelligent plant protection system. The system is composed of a wireless lens, temperature and humidity sensor, intelligent information terminal, and probe rod to realize the collection of plant images and meteorological information. At the same time, a software based on the mobile terminal and the computer terminal was developed. The plant images and meteorological data are transmitted to the server through Wi-Fi transmission. Combined with the expert knowledge model, a solution is generated, and the user can identify the current diseases and pests and obtain solutions at any time. The system can remotely and automatically monitor and warn of mainstream diseases and pests of field crops such as rice and wheat and provide support for fine plant protection management. [ABSTRACT FROM AUTHOR]

Agricultural eco-efficiency can effectively reflect the coordinated green and balanced development of rural resources, and digital financial inclusion provides a strong financial power in the process of long-term balanced development of rural areas. There may be a complex relationship between the two. Therefore, based on the panel data of 30 provinces, autonomous regions, and municipalities in mainland China from 2011 to 2018, this paper explores the impact of digital financial inclusion on agricultural eco-efficiency through the differential GMM method. Further, the paper analyzes how digital financial inclusion influences agricultural eco-efficiency by influencing the agricultural scientific and technological investment. The following conclusions are drawn. First, there is a positive U-shaped nonlinear relationship between digital financial inclusion and agricultural eco-efficiency. Second, the impact of digital financial inclusion on agricultural eco-efficiency is of regional heterogeneity. Digital financial inclusion has a significant positive U-shaped impact on agricultural eco-efficiency in central China but has no significant impact on Eastern and Western China. Third, agricultural R&D investment will intensify the promotion effect of digital financial inclusion on agricultural eco-efficiency. [ABSTRACT FROM AUTHOR]

Featured Application: This paper conducts a literature review on information and communication solutions supporting data lifecycle in precision agriculture. The role of agriculture in environmental degradation and climate change has been at the center of a long-lasting and controversial debate. This situation combined with the expected growth in crop demand and the increasing prices of fertilizers and pesticides has made the need for a more resource-efficient and environmentally sustainable agriculture more evident than ever. Precision agriculture (PA), as a relatively new farming management concept, aims to improve crop performance as well as to reduce the environmental footprint by utilizing information about the temporal and the spatial variability of crops. Information and communication technology (ICT) systems have influenced and shaped every part of modern life, and PA is no exception. The current paper conducts a literature review of prominent ICT solutions, focusing on their role in supporting different phases of the lifecycle of PA-related data. In addition to this, a data lifecycle model was developed as part of a novel categorization approach for the analyzed solutions. [ABSTRACT FROM AUTHOR]

Integrated farm management (IFM) is promoted as a whole farm approach toward Agriculture 4.0, incorporating smart farming technologies for attempting to limit livestock production's negative impacts on the environment while increasing productivity with regard to the economic viability of rural communities. The Internet of Things (IoT) may serve as an enabler to ensure key properties—such as interconnectivity, scalability, agility, and interoperability—in IFM systems so that they could provide object-based services while adapting to dynamic changes. This paper focuses on the problem of facilitating the management, processing, and sharing of the vast and heterogeneous data points generated in livestock facilities by introducing distributed IoT middleware as the core of a responsive and adaptive service-oriented IFM system, specifically targeted to enable smart livestock farming in view of its unique requirements. The proposed IoT middleware encompasses the context-awareness approach via the integration of a flexible ontology-based structure for modeling and reasoning. The IoT middleware was assessed in actual conditions on the grounds of a case study for smart control of the thermal environment in a medium-sized pig farming facility. As derived from the obtained evaluation results, the system appears to perform quite satisfactorily in terms of computational performance as well as ontology coherence, consistency, and efficiency. [ABSTRACT FROM AUTHOR]

The cooperative is one of the most important forms of business in the agricultural sector, due to its special characteristics for small farmers and livestock producers in order to gain access to greater comparative advantages. In addition, cooperatives are a driving force in the social economy, which means that investment in agricultural cooperatives can be seen as a sustainable investment. The aim of this paper is to analyse the preferences of investors in agricultural company cooperatives, looking in depth at the role of the cooperative as a business form. In order to achieve this objective, the choice experiment methodology was applied by carrying out a questionnaire to a total of 282 investors. Latent class models were also used to identify possible groups of investors. Two classes of investors have been identified based on their preferences: owners (return seeking) and workers (risk averse). [ABSTRACT FROM AUTHOR]

Farming faces challenges that increase the adverse effects on farms' economics, labor, and the environment. Smart farming technologies (SFTs) are expected to assist in reverting this situation. In this work, 1064 SFTs were derived from scientific papers, research projects, and industrial products. They were classified by technology readiness level (TRL), typology, and field operation, and they were assessed for their economic, environmental, and labor impact, as well as their adoption readiness from end-users. It was shown that scientific articles dealt with SFTs of lower TRL than research projects. In scientific articles, researchers investigated mostly recording technologies, while, in research projects, they focused primarily on farm management information systems and robotic/automation systems. Scouting technologies were the main SFT type in scientific papers and research projects, but variable rate application technologies were mostly located in commercial products. In scientific papers, there was limited analysis of economic, environmental, and labor impact of the SFTs under investigation, while, in research projects, these impacts were studied thoroughly. Further, in commercial SFTs, the focus was on economic impact and less on labor and environmental issues. With respect to adoption readiness, it was found that all of the factors to facilitate SFT adoption became more positive moving from SFTs in scientific papers to fully functional commercial SFTs, indicating that SFTs reach the market when most of these factors are addressed for the benefit of the farmers. This SFT analysis is expected to inform researchers on adapting their research, as well as help policy-makers adjust their strategy toward digitized agriculture adoption and farmers with the current situation and future trends of SFTs. [ABSTRACT FROM AUTHOR]

Intellectual capital (IC) has become a crucial strategic resource in the knowledge economy. The purpose of this study is to understand the IC-financial performance relationship of listed Chinese agricultural companies. This paper uses the original value added intellectual coefficient (VAIC) model, the adjusted VAIC (AVAIC) model, and the modified VAIC (MVAIC) model to measure IC. The results show a positive and significant relationship between IC and financial performance (return on assets and return on equity) in three models. Additionally, human capital and physical capital are two major driving forces. In the AVAIC model, innovation capital exerts a positive impact on financial performance, whereas this impact is not significant at the 5% level in the MVAIC model. The results suggest that further improvements in IC measurement are still needed. This study has important implications for both academia and industry regarding IC measurement. [ABSTRACT FROM AUTHOR]

Today, precision agriculture technologies (PATs) can be considered a tool for the management of the farm which allows the agricultural entrepreneur to optimise inputs, reduce costs, and offer the best quantitative and qualitative agricultural products. In Italy, the number of digital farmers is still low; therefore, it is not yet possible to assess with certainty the actual economic benefits that technologies bring to the farm. To bridge this gap, the paper proposes, through the analysis of a case study, an assessment of the economic efficiency of an Italian cereal farm that has invested in precision agriculture. The results reveal that, unlike what is reported in the literature, after the technological adoption, the farm keeps both the yield and variable costs stable. However, the major benefit is recorded in the decrease in labour costs (−20%) and in the reduction of pesticides (−53%). The increase in the quantity of nitrogen (+11%) and of seed distributed in the field (+5%) indicates that, in the face of a significant increase in total costs due to the capital invested in technology, the farm aims to intensify production rather than reduce agricultural inputs. [ABSTRACT FROM AUTHOR]

Recently, the development of agricultural technology has been focused on achieving higher reliability and quality of work. The aim of the presented paper was to examine the possibilities of evaluating the quality of work of wide-area irrigation machinery by monitoring the coefficients of uniformity and non-uniformity of irrigation. The object of the research was pivot irrigation machinery equipped with sprinklers with a total length from 230 to 540 m. The commonly applied quality of work parameter for wide-range irrigators is the irrigation uniformity coefficient according to Heermann and Hein CUH. Work quality evaluations were also carried out through other parameters applicable in practice, such as irrigation uniformity coefficients calculated according to Christiansen CU, Wilcox and Swailes Cws, and our introduced parameters, the coefficient ar (derived from the degree of unevenness according to Oehler) and the degree of uniformity γr (derived from the degree of non-uniformity according to Voight). Other applied parameters for determining the quality of work of wide-range irrigation machinery were the coefficients of irrigation uniformity according to Hart and Reynolds CUhr, further according to Criddle CUcr and subsequently according to Beale and Howell CUbr. Next, the parameters of the non-uniformity coefficient according to Oehler a, the coefficient of variation according to Stefanelli Cv, the degree of non-uniformity according to Voigt γ and the degree of non-uniformity according to Hofmeister Ef were evaluated. Field tests were performed during the growing season of cultivated crops (potatoes, corn and sugar beet) in the village of Trakovice (agricultural enterprise SLOV-MART, southwest of the Slovakia) and in the district of Piešťany (Agrobiop, joint stock company). During the research, the inlet operating parameters (speed stage, inlet pressure, irrigation dose), technical parameters (number of sprayers, total length, number of chassis) and weather conditions (wind speed and temperature) were recorded. The obtained results were examined by one-way ANOVA analysis depending on the observed coefficient or input conditions and subsequently verified by Tukey and Duncan tests as needed. Irrigation uniformity values ranged from 67.58% (Cws) to 95.88% (CUbh) depending on the input conditions. Irrigation non-uniformity values ranged from 8.58 (a, Ef) to 32.42% (Cv). The results indicate a statistically significant effect of the site of interest and thus the impact of particular field conditions (p < 0.05). When evaluating the application of different coefficients of irrigation uniformity, the results showed a statistically significant effect only in the first test (p = 0.03, p < 0.05). During further repeated measurements, the quality of work increased due to the performed inspection of all sprayers and the reduction in the influence of the wind. [ABSTRACT FROM AUTHOR]

There has been an explosion of innovation in agricultural technologies, but whether the anticipated benefits are fully realised depends partly upon with the institutional structures are supportive. Many types of law will shape what innovations are viable and the scale of the economic returns. Australia does not have a coherent strategy for future rural regulation that will both minimise the public risks and increase the private opportunities from future agricultural innovation. This paper addresses these issues. It considers the diverse legal issues that will affect these opportunities, and it looks particularly at agricultural robotics as an example of the many ways in which legal issues will shape opportunities from innovation. It proposes that an integrated strategy, based on a careful analysis of future issues, would be a significant contributor to Australia's agricultural sector and to its innovating industries. [ABSTRACT FROM AUTHOR]

In this work, an exhaustive revision is given of the literature associated with advanced information and communication technologies in agriculture within a window of 25 years using bibliometric tools enabled to detect of the main actors, structure, and dynamics in the scientific papers. The main findings are a trend of growth in the dynamics of publications associated with advanced information and communication technologies in agriculture productivity. Another assertion is that countries, like the USA, China, and Brazil, stand out in many publications due to allocating more resources to research, development, and agricultural productivity. In addition, the collaboration networks between countries are frequently in regions with closer cultural and idiomatic ties; additionally, terms' occurrence are obtained with Louvain algorithm predominating four clusters: precision agriculture, smart agriculture, remote sensing, and climate smart agriculture. Finally, the thematic-map characterization with Callon's density and centrality is applied in three periods. The first period of thematic analysis shows a transition in detecting the variability of a nutrient, such as nitrogen, through the help of immature georeferenced techniques, towards greater remote sensing involvement. In the transition from the second to the third stage, the maturation of technologies, such as unmanned aerial vehicles, wireless sensor networks, and the machine learning area, is observed. [ABSTRACT FROM AUTHOR]