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Bibliometric analysis explores large volume of scientific data, revealing trends and insights in a specific research field. Consistently, a bibliometric analysis of 30 years (1990–2020) was performed within the US corn (Zea mays L.) production using the Scopus database and VOSviewer. Search query was performed within the article title, abstract, and keywords indicative of management practices in corn. Exclusion criterion based on subject area and journals generated a total of 7468 publications. The data analysis revealed contributions from 7327 authors and 47 organizations documented in 69 journals. The top five organizations leading the investigation were United States Department of Agriculture – Agricultural Research Service, Iowa State University, University of Nebraska, University of Illinois, and Purdue University. The most prolific authors were Dr. Rattan Lal (Ohio State University, Columbus, OH), Dr. Douglas L. Karlen (USDA‐ARS, Ames, IA), Dr. Kenneth G. Cassman (University of Nebraska, Lincoln, NE), Dr. Lajpat Rai Ahuja (USDA‐ARS, Ft. Collins, CO), and Dr. John Walsh Doran (USDA‐ARS, Lincoln, NE). Journals with most publications were Agronomy Journal; Soil Science Society of America Journal; Soil and Tillage Research; Crop Science; and Agriculture, Ecosystems & Environment. Furthermore, author keywords differed from queried keywords, and no‐till, nitrogen, cover crop, soybean, irrigation, phosphorus, conservation tillage, yield, and water quality were most prominent. Moreover, there was an evident shift in keywords and an observed trend between 1998 and 2020. Overall, these findings allow researchers to explore network maps via the hyperlinks present in papers, identifying research gaps and advancing original studies to bridge gaps in the literature. Core Ideas: There has been a consistent increase in corn research in last 30 years.United States Department of Agriculture – Agricultural Research Service (USDA‐ARS) was the top funding sponsor and leading investigator.Queried keywords differed from author keywords, and a trend was observed in their use.The top journal with most publications on corn production was Agronomy Journal. [ABSTRACT FROM AUTHOR]

Alfalfa (Medicago sativa L.) is one of the most important forage crops in the world. Drought is recognized as a major challenge limiting alfalfa production and threatening food security. Although some literature reviews have been conducted in this area, bibliometric reviews based on large amounts of published data are still lacking. In this paper, a bibliometric analysis of alfalfa drought stress from 1998-2023 was conducted using the Web of Science Core Collection database in order to assess global trends in alfalfa drought stress research and to provide new directions for future research. The results showed that the annual publication output maintained an increase in most years, with China and the United States contributing significantly to the field. Most of the journals published are specialized journals in botany, environmental science, soil science and crop science, as well as related agribusiness journals. "plant growth" and "yield" were the most frequently used keywords, reflecting the important purpose of research in this field. And two main research directions were identified: research on drought response mechanism of alfalfa and exploration of drought-resistant technology. In addition, physiological, biochemical, and molecular responses of drought tolerance and high yield in alfalfa, transgenics, and microbial fertilizer research have been hot research topics in recent years and may continue in the future. The ultimate goal of this paper is to provide a foundational reference for future research on alfalfa's drought resistance and yield optimization mechanisms, thereby enhancing the crop's application in agricultural production. [ABSTRACT FROM AUTHOR]

The editorial board of Crop, Forage & Turfgrass Management has announced the recipients of the 2023 Editor's Citation for Excellence. These awards recognize the dedication and commitment of volunteer reviewers and editors who have contributed to the high standard and quality of papers published in the journal. The recipients were chosen based on their thorough and timely reviews or editing of manuscripts. Each recipient will receive a certificate of appreciation, a gift certificate, and recognition in CSA News magazine. The recipients include Dr. Thomas Butts, Dr. Murali Darapuneni, Dr. Adam Thoms, Harkirat Kaur, Dr. Michael Richardson, and Dr. Brett Rushing. [Extracted from the article]

The Journal of Plant Registrations has announced the recipients of the 2023 Editor's Citation for Excellence. These awards recognize the dedication and professional commitment of volunteer reviewers and editors who have contributed to the high standard and quality of papers published in the journal. The recipients were nominated based on their thorough, competent, and timely reviews or editing of manuscripts. The recipients include Dr. Hussein Abdel-Haleem, a plant geneticist focusing on improving oilseeds and industrial crops; Dr. Mary Guttieri, a research geneticist specializing in hard winter wheat germplasm development; Dr. Jodi Scheffler, a cotton geneticist known for her breeding research; Dr. James Beaver, a retired professor with expertise in breeding common beans; Dr. Phil Miklas, a dry bean breeder and geneticist; and Dr. Paul White, a research leader studying sustainable sugarcane production. [Extracted from the article]

Unmanned aerial vehicle-based hyperspectral imaging (HSI) and machine learning (ML) techniques are applied for crop field data collection such as yield, nitrogen content, leaf chlorophyll, biomass estimation, leaf area index (LAI), and biotic and abiotic stress. It is very difficult to identify with certainty best practices for specific applications and for targeted crop species because the results are frequently contradictory; models or indices sometimes perform well, but other times they do not. ML and HSI are useful and sophisticated techniques, but treating them as first-option methods may be detrimental in the long term. Favoring HSI in situations where a multispectral sensor performs equally well only delays end-user adoption of technology owing to cost and complexity. The past few years have seen increased interest in unmanned aerial vehicle (UAV)-based hyperspectral imaging (HSI) and machine learning (ML) in agricultural research, concomitant with an increase in published research on these topics. We provide an updated review, written for agriculturalists, highlighting the benefits in the retrieval of biophysical parameters of crops via UAVs relative to less sophisticated options. We reviewed >70 recent papers and found few consistent results between similar studies. Owing to their high complexity and cost, especially when applied to crops of low value, the benefits of most of the research reviewed are difficult to explain. Future effort will be necessary to distill research findings into lower-cost options for end-users. [ABSTRACT FROM AUTHOR]

The editorial board of Crop Science has announced the recipients of the 2023 Editor's Citation for Excellence. These awards recognize the dedication and professional commitment of volunteer reviewers and editors who have contributed to the high standard and quality of papers published in the journal. The recipients were chosen based on their thorough, competent, and timely reviews or editing of manuscripts. Each recipient will receive a certificate of appreciation, an ASA‐CSSA‐SSSA gift certificate, and recognition in CSA News. The recipients include Dr. Gabriela Abeledo, Dr. Brenda Laura Gambin, Dr. Lori Hinze, Dr. Vince Pantalone, Dr. Cristiane Pilon, and Dr. Wei Wu as associate editors, and Dr. Georgia Eizenga, Dr. Jales Mendes Oliveira Fonseca, Dr. Laura Lindsey, Dr. Ntombokulunga Mbuma, Dr. Desalegn Serba, and Prof. Dr. Meiliang Zhou as reviewers. [Extracted from the article]

Wheat is the main grain crop in our country, and the traditional wheat yield estimation method is time-consuming and laborious. By estimating wheat yield efficiently, quickly and non-destructively, agricultural producers can quickly obtain information about wheat yield, manage wheat fields more scientifically and accurately, and ensure national food security. Taking the Xinxiang Experimental Base of the Crop Science Research Institute, Chinese Academy of Agricultural Sciences as an example, hyperspectral data for the critical growth stages of wheat were pre-processed. A total of 27 vegetation indices were calculated from the experimental plots. These indices were then subjected to correlation analysis with measured wheat yield. Vegetation indices with Pearson correlation coefficients greater than 0.5 were selected. Five methods, including multiple linear regression, stepwise regression, principal component regression, neural networks and random forests, were used to construct wheat yield estimation models. Among the methods used, multiple linear regression, stepwise regression and the models developed using principal component analysis showed a lower modelling accuracy and validation precision. However, the neural network and random forest methods both achieved a modelling accuracy R2 greater than 0.6, with validation accuracy R2 values of 0.729 and 0.946, respectively. In addition, the random forest method had a lower cross-validation RMSE value, with values of 869.8 kg/hm−2, indicating a higher model accuracy. In summary, the random forest method provided the optimal estimation for wheat yield, enabling the timely and accurate pre-harvest wheat yield prediction, which has significant value for precision agriculture management and decision making. [ABSTRACT FROM AUTHOR]

Agriculture constitutes a sector with a considerable environmental impact, a concern that is poised to increase with the projected growth in population, thereby amplifying implications for public health. Effectively mitigating and managing this impact demands the implementation of intelligent technologies and data-driven methodologies collectively called precision agriculture. While certain methodologies enjoy widespread acknowledgement, others, despite their lesser prominence, contribute meaningfully. This mini-review report discusses the prevalent AI technologies within precision agriculture over the preceding five years, with a specific emphasis on crop yield prediction and disease detection domains extensively studied within the current literature. The primary objective is to give a comprehensive overview of AI applications in agriculture, spanning machine learning, deep learning, and statistical methods. This approach aims to address a notable gap wherein existing reviews predominantly focus on singular aspects rather than presenting a unified and inclusive perspective. [ABSTRACT FROM AUTHOR]

In a growing world population scenario, greater demand for food and in turn agricultural input is expected in the coming decades. The development of innovative sustainable amendments and fertilizers such as hydrogels (HGs) relies on this context. HGs are defined as 3D polymeric networks with the ability to absorb and retain a large amount of water (i.e., swellability). HGs swellability makes them ideal platforms for water- and nutrient-controlled release. The literature reflects a notable diversity in HGs composition, properties, and impacts on agroecosystems, resulting in the dissemination of information across multiple publications. Within this context, the primary aim of this scientific review is to systematically compile, critically evaluate, and synthesize the latest findings and relevant literature pertaining to the application of HGs in agroecosystems. We found that this class of materials was consistently found on soil bulk density, crop available water, soil electrical conductivity, cation exchange capacity and main cereal or legume crops performances particularly under moderate water deficit conditions, suggesting their effectiveness for arid and semi- arid areas. Encouraging results were also found on pesticide, agrochemicals, and microorganisms delivering. Thus, the delivery of agronomic inputs with loaded-HG may play a key role in forthcoming agriculture that would minimize the agronomic impact on the environment. Nevertheless, some critical issues still remain open, concerning: (i) the decreased of HG effectiveness following wetting and drying cycles, (ii) the impact of Na+ contained in HG on crop cells, (iii) the HG effect on soil saturated hydraulic conductivity and pH, and (iv) HG biodegradability and their fate into the environment. In conclusion, HGs seem a promising technology to reach/maintain food security and soil health, but future research should address the development of well-defined protocols for producing biowaste-derived HG that will ensure their biodegradability and non-toxicity and will disclose new insights into a circular economy approach agronomy. One of the biggest challenges would be the synthesis of “smart” HG able to respond to environmental stimuli triggering molecule(s) release in soil. [ABSTRACT FROM AUTHOR]