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As arid lands expand across the globe, scholars increasingly turn to the archaeological record for examples of sustainable farming in extreme environments. The arid north coast of Peru was the setting of early and intensive irrigation-based farming; it is also periodically impacted by sudden, heavy rainfall related to the El Niño Southern Oscillation (ENSO) phenomenon. While the sociopolitical effects, technologies, and engineering expertise of these irrigation systems have been thoroughly examined and theorized, little is known about how farmers managed periods of water stress. The aim of this study is to test whether arid zone farming was supported by hybrid, intermittent flood and perennial water source systems in the prehispanic past. An arroyo in the Chicama Valley was selected for preliminary data collection, and these data are presented here: (1) drone photography of the arroyo capturing the aftermath of a recent (2023) rain event; and (2) potassium (K) soil test kit results from samples collected near suspected prehispanic check dam features in the same area. The paper combines these data with comparative examples from the literature to suggest that the prehispanic features functioned as water-harvesting infrastructure. The paper concludes that sustainable, arid zone farming can be supported by hybrid, intermittent flood and perennial water source systems. [ABSTRACT FROM AUTHOR]

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]

This document discusses the importance of microbial communities in agriculture and the use of metagenomic analysis to understand and utilize the agricultural microbiome. Microorganisms in the soil, such as bacteria and fungi, play a crucial role in nutrient cycling, plant growth promotion, disease control, and resilience to environmental stresses. The document highlights various research papers that explore the impact of microorganisms on crop management, disease dynamics, and soil health. The findings emphasize the potential of metagenomic technologies in advancing sustainable farming practices and promoting ecological sustainability. The document also provides links to a special issue on the topic and invites further research contributions. [Extracted from the article]

Due to the changes in the agricultural sector, there is a demand among policymakers, administration, advisors and farmers for comparisons of the economic efficiency of organic versus conventional farms and their environmental impact. The authors of the paper hypothesised that in some conditions, organic farms can reach similar productive and economic results as conventional farms and, at the same time, achieve better environmental effects. The aim of the research was to compare the production, environmental and economic effects of selected organic and conventional farms from eastern Poland (mixed, crop production, animal production). The basis for the comparative analysis was the data from 12 farms obtained using the questionnaire method (direct interview) from the years 2020 to 2021. The yields of cereals in the tested organic farms were about one-third lower than the average obtained on conventional farms. Total organic crop production in cereal units per ha was 43% of conventional production. Balances of NPK indicated surpluses or deficiency, which suggested that in both systems nutrient management should be improved and optimised. The compared groups of organic farms generally had higher economic efficiency—both with and without subsidies—than conventional farms, despite the fact that the latter obtained significantly higher incomes. Gross farm income on conventional farms was higher than on organic farms by 28%, but conventional farms had higher direct costs by 332% than organic ones. As a result, the economic efficiency of agricultural production of organic farms was higher by 59% than conventional farms. The lower level of inputs incurred on organic farms was the main factor determining their high economic efficiency. Organic farms pursuing an intensive model of agricultural production (milk production), but also with specialisation in crop production, proved to be the most economically efficient. Despite diversified production, reducing the income risk, the mixed production, both organic and conventional, in the studied set of farms turned out to be the least profitable, indicating the need to support it. It is suggested that moderate specialisation increases the efficiency of management in organic farming. [ABSTRACT FROM AUTHOR]

To control heavy metal pollution effectively, prevention of heavy metal accumulations in agricultural soils should be the priority rather than remediation of heavy metal contamination. In this research, papers which contained input and output fluxes of Cd, As, Cr, Hg, and Pb in topsoil (the plough layer) of agricultural lands in Hunan, Zhejiang, the Yangtze River Delta, Hainan, and China as a whole were explored. Fluxes of heavy metal species were recalculated, and future trends of pollution were predicted. Also, cases regarding the application of technologies to control the input and output of heavy metals were analyzed. Results indicated that atmospheric decomposition was the dominant input source of heavy metals in all study sites except Hainan. The relative contributions of fertilizers, irrigation water, and straw returning fluctuated greatly among different sites. Cd pollution in all sites was the most serious, followed by Cr and Pb. In Hunan, Cd has already exceeded the maximum limit value and needs to be controlled urgently. The input of heavy metals from irrigation water, fertilizers, and straw returning could be controlled by proposing more policies to manage their quality and application amounts. The amount of heavy metals absorbed by plants could be increased by cultivating crops with hyperaccumulators and high-biomass plants. [ABSTRACT FROM AUTHOR]

Weeds and crops engage in a relentless battle for the same resources, leading to potential reductions in crop yields and increased agricultural costs. Traditional methods of weed control, such as heavy herbicide use, come with the drawback of promoting weed resistance and environmental pollution. As the demand for pollution-free and organic agricultural products rises, there is a pressing need for innovative solutions. The emergence of smart agricultural equipment, including intelligent robots, unmanned aerial vehicles and satellite technology, proves to be pivotal in addressing weed-related challenges. The effectiveness of smart agricultural equipment, however, hinges on accurate detection, a task influenced by various factors, like growth stages, environmental conditions and shading. To achieve precise crop identification, it is essential to employ suitable sensors and optimized algorithms. Deep learning plays a crucial role in enhancing weed recognition accuracy. This advancement enables targeted actions such as minimal pesticide spraying or precise laser excision of weeds, effectively reducing the overall cost of agricultural production. This paper provides a thorough overview of the application of deep learning for crop and weed recognition in smart agricultural equipment. Starting with an overview of intelligent agricultural tools, sensors and identification algorithms, the discussion delves into instructive examples, showcasing the technology's prowess in distinguishing between weeds and crops. The narrative highlights recent breakthroughs in automated technologies for precision plant identification while acknowledging existing challenges and proposing prospects. By marrying cutting-edge technology with sustainable agricultural practices, the adoption of intelligent equipment presents a promising path toward efficient and eco-friendly weed management in modern agriculture. [ABSTRACT FROM AUTHOR]

Nanotechnology, nanosensors in particular, has increasingly drawn researchers' attention in recent years since it has been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change and sustainability, have promoted such attention and pushed forward the use of nanosensors in agroindustry and environmental applications. However, issues with noise and confounding signals make the use of these tools a non-trivial technical challenge. Great advances in artificial intelligence, and more particularly machine learning, have provided new tools that have allowed researchers to improve the quality and functionality of nanosensor systems. This short review presents the latest work in the analysis of data from nanosensors using machine learning for agroenvironmental applications. It consists of an introduction to the topics of nanosensors and machine learning and the application of machine learning to the field of nanosensors. The rest of the paper consists of examples of the application of machine learning techniques to the utilisation of electrochemical, luminescent, SERS and colourimetric nanosensor classes. The final section consists of a short discussion and conclusion concerning the relevance of the material discussed in the review to the future of the agroenvironmental sector. [ABSTRACT FROM AUTHOR]

The domestication of vines started in Asia 11,000 years ago, although it was not until the 19th century that oenology was established as a scientific discipline thanks to the research of Louis Pasteur on the role of microorganisms in wine fermentation. At the present time, the progression in next-generation sequencing (NGS) technologies is helping to facilitate the identification of microbial dynamics during winemaking. These advancements have aided winemakers in gaining a more comprehensive understanding of the role of microbiota in the fermentation process, which, in turn, is ultimately responsible for the delivery of provisioning (wine features and its production), regulating (such as carbon storage by vineyards, regulation of soil quality, and biocontrol of pests and diseases) or cultural (such as aesthetic values of vineyard landscapes, scholarly enjoyment of wine, and a sense of belonging in wine-growing regions) ecosystem services. To our knowledge, this is the first review of the state of knowledge on the role of microbiota in the delivery of ecosystem services in the wine sector, as well as the possibility of valuing them in monetary terms by operating logic chains, such as those suggested by the SEEA-EA framework. This paper concludes with a review of management practices that may enhance the value of microbiota ecosystem services and the role of smart farming in this task. [ABSTRACT FROM AUTHOR]

Weed harrowing is commonly used to manage weeds in organic farming but is also applied in conventional farming to replace herbicides. Due to its whole-field application, weed harrowing after crop emergence has relatively poor selectivity and may cause crop damage. Weediness generally varies within a field. Therefore, there is a potential to improve the selectivity and consider the within-field variation in weediness. This paper describes a decision model for precision post-emergence weed harrowing in cereals based on experimental data in spring barley and nonlinear regression analysis. The model predicts the optimal weed harrowing intensity in terms of the tine angle of the harrow for a given weediness (in terms of percentage weed cover), a given draft force of tines, and the biological weed damage threshold (in terms of percentage weed cover). Weed cover was measured with near-ground RGB images analyzed with a machine vision algorithm based on deep learning techniques. The draft force of tines was estimated with an electronic load cell. The proposed model is the first that uses a weed damage threshold in addition to site-specific values of weed cover and soil hardness to predict the site-specific optimal weed harrow tine angle. Future field trials should validate the suggested model. [ABSTRACT FROM AUTHOR]

The leading role of Italy in the cultivation of durum wheat stimulated intense breeding activities in the country from the beginning of the 20th century, much earlier than in any other country involved in durum wheat production. Older, genetically more heterogeneous landraces were replaced with new, highly productive, superior quality varieties, and this led to an inevitable reduction in the overall genetic diversity among new cultivars, which makes the genetic variability preserved in old cultivars particularly valuable and important. The aim of this paper was to assist future breeding programs by providing a detailed description of the history of durum wheat breeding in Italy and of the changes in yield, quality, and related traits that subsequently occurred, starting from the most diffuse landraces present between 1900 and 1920 up until the present day. The parallel evolution of breeding techniques, breeding goals, and agricultural systems in this period is also described, and some future breeding goals suggested. In the current context of climate change and of rapidly mutating pathogen populations, preserving the yield level through the continuous introduction of new cultivars by exploiting the reservoir of largely unused genetic variation stored in old cultivars and landraces could be as important as increasing grain yield and quality. [ABSTRACT FROM AUTHOR]

Strawberry (Fragaria × ananassa Duch.) has been widely accepted as the "Queen of Fruits". It has been identified as having high levels of vitamin C and antioxidants that are beneficial for maintaining cardiovascular health and maintaining blood sugar levels. The implementation of advanced techniques like precision agriculture (PA) is crucial for enhancing production compared to conventional farming methods. In recent years, the successful application of deep learning models was represented by convolutional neural networks (CNNs) in a variety of disciplines of computer vision (CV). Due to the dearth of a comprehensive and detailed discussion on the application of deep learning to strawberry cultivation, a particular review of recent technologies is needed. This paper provides an overview of recent advancements in strawberry cultivation utilizing Deep Learning (DL) techniques. It provides a comprehensive understanding of the most up-to-date techniques and methodologies used in this field by examining recent research. It also discusses the recent advanced variants of the DL model, along with a fundamental overview of CNN architecture. In addition, techniques for fine-tuning DL models have been covered. Besides, various strawberry-planting-related datasets were examined in the literature, and the limitations of using research models for real-time research have been discussed. [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]