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Copyright of Journal of Henan Agricultural Sciences is the property of Editorial Board of Journal of Henan Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R2) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies. [ABSTRACT FROM AUTHOR]

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]

This paper advances understanding of the impacts of climate change on crops in China by moving from ex-post analysis to forecasting, and by demonstrating how the effects of climate change will affect the growth period and the planting boundaries of winter wheat. Using a multiple regression model based on agricultural meteorological observations and the IPCC AR5 GCMs simulations, we find that the sowing date of winter wheat in the base period, 2040s and 2070s, shows a gradually delayed trend from north to south and the growth period of winter wheat in China will be shortened under climate change. The simulation results also show that (i) the north planting boundaries of winter wheat in China will likely move northward and expand westward in the future, while the south planting boundary will rise and spread in south Hainan and Taiwan; and (ii) the Xinjiang Uygur Autonomous Region and the Inner Mongolia Autonomous Region will have the largest increases in planting areas in 2040s and 2070s. Our simulation implies that Xinjiang and Inner Mongolia are more sensitive to climate change than other regions in China and priority should be given to design adaptation strategies for winter wheat planting for these provinces. [ABSTRACT FROM AUTHOR]

Seed traits (ST) influence seedling establishment, population dynamics, community composition and ecosystem function and reflect the adaptability of plants and the environmental conditions they experienced. There has been a historical and global accumulation of studies on ST, but with few pertaining to visual and quantitative analyses. To understand the trends in the field of ST research in the past 30 years, we conducted a bibliometric analysis based on the Science Citation Index-Expanded (SCI-E) database. The analysis provided annual publications, time trends for keywords, the most productive journals, authors, institutions and countries, and a comprehensive overview of the ST field. Our results showed that in the past 30 years, the number of publications in ST research has increased at an average annual growth rate of 9.1%, while the average number of citations per paper per year showed a rapid increase–slow increase–decrease trend. Keyword analysis showed that "germination" was the most popular research section. Crop Science ranked first among the top journals and Theoretical and Applied Genetics had greater influence in this area and more citations than other journals. The 10 most productive institutions were mostly located in the United States, China and Australia. Furthermore, the three countries also had the largest number of publications and citations. Our analysis showed that the research interests in ST have evolved from genetics and agricultural science to ecological research over the last thirty years; as more fields embrace ST research, there are opportunities for international and interdisciplinary collaborations, cooperative institutions and new advances in the field. [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]

This article is part of a series of brief commentaries to highlight papers that have resulted in important and distinctly new perspectives in crop science. A criterion for selection of papers is that they must have been published at least 20 yr ago to allow for a long‐range perspective in assessment of the papers. The current article briefly reviews the paper by Harold L. Penman published in 1948 that offered an original analysis of approaches to estimate evaporation from natural surfaces. He developed two approaches: one based on a sink strength model, and the second on an energy balance model. He compared the predictions of these approaches to his experimental observations of evaporation from open water, bare soil, and grass. He found each of the models did well (r ≈ 0.8) in predicting open water evaporation. The evaporation from bare soil and grass was less than that from the open water, and the predicted evaporation amounts were variable even when normalized by open water evaporation. Even though Penman warned that aspects of the approaches were empirical, the energy balance model remains a mainstay in predicting crop water loss. [ABSTRACT FROM AUTHOR]

An evolving green agenda as the UK seeks to achieve 'net zero' in greenhouse gas emissions by 2050, coupled with our new trading relationship with the European Union, is resulting in new government policies, which will be disruptive to Britain's traditional food and farming practices. These policies encourage sustainable farming and land‐sparing to restore natural habitats and will provide an opportunity to address issues such as high emissions of GHGs and dwindling biodiversity resulting from many intensive agricultural practices. To address these and other food challenges such as global conflicts and health issues, Britain will need a revolution in its food system. The aim of this paper is to make the case for such a food revolution where additional healthy food for the UK population is produced in‐country in specialised production units for fruits and vegetables developed on sites previously considered unsuitable for crop production. High crop productivity can be achieved in low‐cost controlled environments, making extensive use of novel crop science and modern controlled‐environment technology. Such systems must be operated with very limited environmental impact. In recent years, growth in the application of plasticulture in UK horticulture has driven some increases in crop yield, quality and value. However, the environmental cost of plastic production and plastic pollution is regarded as a generational challenge that faces the earth system complex. The distribution of plastic waste is ubiquitous, with a significant pollution load arising from a range of agricultural practices. The primary receptor of agriplastic pollution is agricultural soil. Impacts of microplastics on crop productivity and quality and also on human health are only now being investigated. This paper explores the possibility that we can mitigate the adverse environmental effects of agriplastics and thereby exploit the potential of plasticulture to enhance the productivity and positive health impact of UK horticulture. [ABSTRACT FROM AUTHOR]

An attempt has been made to test whether crop science literature follows Bradford law. The dataset comprised 10,100 papers indexed in Indian Science Abstracts and CAB Abstracts during the period 1965-2010. A ranked list of journals has been prepared and Leimkuhler test was conducted. The list of core journals is provided. The dataset does not follow Bradford law even with Leimkuhler formulation. The crops covered are rice, wheat, barley, maize, sorghum and millets. Possibly combining of data for six crops together and cumulating of data of every fifth year deviated the dataset from Bradford law of scattering. [ABSTRACT FROM AUTHOR]

This article is part of a series of brief commentaries to highlight papers that have resulted in important and distinctly new perspectives in crop science. A criterion for selection of papers is that they must have been published at least 20 yr ago to allow for a long‐range perspective in assessment of the papers. The current article briefly reviews the paper by M. Wayne Adams published in 1967 that provided original insights about compensation in the components of seed number and seed yield in determining crop yield. Adams reviewed experimental results in a large number of crop species and discovered a consistent negative correlation between seed number and mean seed mass. He labeled this negative correlation as "component compensation" in the formation of yield. He hypothesized that component compensation resulted from limiting nutrient‐metabolite availability to support developing reproductive structures. Ultimately, the retained seeds would be filled to the extent allowed by the supply of nutrient‐metabolites so that there was compensation between seed number and individual seed mass. [ABSTRACT FROM AUTHOR]

This article is part of a series of brief commentaries to highlight papers that have resulted in important and distinctly new perspectives in crop science. A criterion for selection of papers is that they must have been published at least 20 yr ago to allow for a long‐range perspective in assessment of the papers. The current article briefly reviews the paper by C.M. Donald and J. Hamblin published in 1976 that provided original insights about the role of harvest index (defined by them as the ratio of economic yield to aboveground mass) in influencing crop yield. Donald and Hamblin reviewed experimental results showing that harvest index was relatively stable across variations in plant size, had high heritability, and was associated with high grain yield. Although their observations were confirmed in subsequent tests, inconsistent results in plant selection in breeding programs for high yield based on harvest index has discouraged the use of harvest index as a basis for selection. Nevertheless, high harvest index is now well ingrained as an essential plant characteristic for high crop yields. [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]

In the current research landscape, there are increasing demands for research to be innovative and cutting-edge. At the same time, concerns are voiced that as a consequence of neoliberal regimes of research governance, innovative research becomes impeded. In this paper, I suggest that to gain a better understanding of these dynamics, it is indispensable to scrutinise current demands for innovativeness as a distinct way of ascribing worth to research. Drawing on interviews and focus groups produced in a close collaboration with three research groups from the crop and soil sciences, I develop the notion of a project-innovation regime of valuation that can be traced in the sphere of research. In this evaluative framework, it is considered valuable to constantly re-invent oneself and take 'first steps' instead of 'just' following up on previous findings. Subsequently, I describe how these demands for innovativeness relate to and often clash with other regimes of valuation that matter for researchers' practices. I show that valuations of innovativeness are in many ways bound to those of productivity and competitiveness, but that these two regimes are nevertheless sometimes in tension with each other, creating a complicated double bind for researchers. Moreover, I highlight that also the project-innovation regime as such is not always in line with what researchers considered as a valuable progress of knowledge, especially because it entails a de-valuation of certain kinds of long-term epistemic agendas. I show that prevailing pushes for innovativeness seem to be based on a rather short-sighted temporal imaginary of scientific progress that is hardly grounded in the complex realities of research practices, and that they can reshape epistemic practices in potentially problematic ways. [ABSTRACT FROM AUTHOR]

Crop losses from pests threaten global food security and safety. In the last six decades, pest control using chemical pesticides has resulted in important yield gains per unit area, worldwide. However, the long-term sustainability of chemical pest control has been increasingly thrown into doubt due to the negative impact on human health, biodiversity, and the environment. Consequently, there is an urgent need to improve the science of crop protection in order to tackle the five key challenges of 21st century agriculture holistically: (i) maintaining or improving agricultural productivity, (ii) producing healthy food, (iii) reducing the negative impacts of agriculture on ecosystem and human health, (iv) ensuring the economic viability of farms, and (v) adapting agriculture to climate change. Agroecological Crop Protection (ACP) can be a powerful approach to address these challenges, as we demonstrate in this paper. ACP is the application of the principles of agroecology to crop protection in order to promote virtuous and sustainable changes in agriculture and food systems. ACP combines multiple approaches and disciplines including ecology, agroecology, and Integrated Pest Management. It promotes a crop protection system compatible with healthy agricultural and food systems, agroecological principles and the "one health" approach. We predict that ACP will meet the challenge of pesticide-free agriculture in the future. In this paper, we will first present the scientific, agricultural and social components of ACP. We will then analyze the research approaches, questions, methods and tools needed to adopt ACP. Finally, we suggest key mechanisms to facilitate the transition to ACP, which will ultimately provide sustainable food, feed, and fuel in a context of major global change. [ABSTRACT FROM AUTHOR]

Background: Tobacco industry interference has been identified as the greatest obstacle to the implementation of evidence-based measures to reduce tobacco use. Understanding and addressing industry interference in public health policy-making is therefore crucial. Existing conceptualisations of corporate political activity (CPA) are embedded in a business perspective and do not attend to CPA's social and public health costs; most have not drawn on the unique resource represented by internal tobacco industry documents. Building on this literature, including systematic reviews, we develop a critically informed conceptual model of tobacco industry political activity.Methods and Findings: We thematically analysed published papers included in two systematic reviews examining tobacco industry influence on taxation and marketing of tobacco; we included 45 of 46 papers in the former category and 20 of 48 papers in the latter (n = 65). We used a grounded theory approach to build taxonomies of "discursive" (argument-based) and "instrumental" (action-based) industry strategies and from these devised the Policy Dystopia Model, which shows that the industry, working through different constituencies, constructs a metanarrative to argue that proposed policies will lead to a dysfunctional future of policy failure and widely dispersed adverse social and economic consequences. Simultaneously, it uses diverse, interlocking insider and outsider instrumental strategies to disseminate this narrative and enhance its persuasiveness in order to secure its preferred policy outcomes. Limitations are that many papers were historical (some dating back to the 1970s) and focused on high-income regions.Conclusions: The model provides an evidence-based, accessible way of understanding diverse corporate political strategies. It should enable public health actors and officials to preempt these strategies and develop realistic assessments of the industry's claims. [ABSTRACT FROM AUTHOR]

Seed moisture content (SMC) is an important attribute to seed quality. Maintaining seed dryness throughout supply chain (The Dry Chain) prevents seed germination and quality losses. Ambient relative humidity (RH) and temperature affect seed moisture and thereof seed moisture isotherm. Present study was conducted to compare the moisture adsorption isotherms of wheat, maize, cotton and quinoa seeds packed in hermetic Super Bag and traditional packaging materials including paper, polypropylene (PP), jute and cloth bags. Seeds were incubated at 60, 70, 80 and 90% static RH. Nearly straight line moisture isotherms for all crop seeds were obtained in Super Bag. Seed moisture contents increased in traditional packaging materials with increasing RH. At higher level of RH, moisture contents increased slightly (1–2%) in Super Bag, whereas this increase was much higher in traditional packaging materials (≈9% higher than original SMC at 90% RH). In second study, seeds were dried to 8 and 14% initial seed moisture contents using zeolite drying beads and were stored in hermetic and conventional packaging materials for a period of 18 months. For all crop seeds, germination was severely affected in all packaging materials both at 8 and 14% initial SMC except storage in Super Bag at 8% SMC. Wheat seed stored in Super Bag at 8% SMC almost maintained initial germination while germination of cotton, maize and quinoa seeds declined 7%, 14% and 30% respectively in Super Bag at 8% SMC. Seed storage in Super Bag can help to prevent the significant increase in seed moisture at higher RH as is evident from moisture isotherm study, thus helps to preserve quality of maize, wheat, cotton and quinoa seeds by maintaining The Dry Chain throughout the storage period. [ABSTRACT FROM AUTHOR]

This article is part of a series of brief commentaries to highlight papers that have resulted in important and distinctly new perspectives in crop science. A criterion for selection of papers is that they must have been published at least 20 yr ago to allow for a long‐range perspective in assessment of the papers. The current article briefly reviews the paper by M.Y. Siddiqi, A.D.M. Glass, T.J. Ruth, and T.W. Rufty, Jr. published in 1990 that investigated the kinetics of nitrate influx by barley (Hordeum vulgare L.) seedlings. Using 13NO3−, they documented the existence of two influx systems. One was inducible and nitrate influx saturated under low nitrate concentrations. The second was noninducible and expressed a linear increase in nitrate influx over high nitrate concentrations. Key transformative results from this study were the demonstration that both systems were under negative feedback control based on root nitrate concentration. [ABSTRACT FROM AUTHOR]

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]

In natural capital accounting, ecosystems are assets that provide ecosystem services to people. Assets can be measured using both physical and monetary units. In the international System of Environmental-Economic Accounting, ecosystem assets are generally valued on the basis of the net present value of the expected flow of ecosystem services. In this paper we argue that several additional conceptualisations of ecosystem assets are needed to understand ecosystems as assets, in support of ecosystem assessments, ecosystem accounting and ecosystem management. In particular, we define ecosystems’ capacity and capability to supply ecosystem services, as well as the potential supply of ecosystem services. Capacity relates to sustainable use levels of multiple ecosystem services, capability involves prioritising the use of one ecosystem service over a basket of services, and potential supply considers the ability of ecosystems to generate services regardless of demand for these services. We ground our definitions in the ecosystem services and accounting literature, and illustrate and compare the concepts of flow, capacity, capability, and potential supply with a range of conceptual and real-world examples drawn from case studies in Europe and North America. Our paper contributes to the development of measurement frameworks for natural capital to support environmental accounting and other assessment frameworks. [ABSTRACT FROM AUTHOR]

One of the main manifestations of climate change will be increased rainfall variability. How to deal with this in agriculture will be a major societal challenge. In this paper we explore flexibility in land use, through deliberate seasonal adjustments in cropped area, as a specific strategy for coping with rainfall variability. Such adjustments are not incorporated in hydro-meteorological crop models commonly used for food security analyses. Our paper contributes to the literature by making a comprehensive model assessment of inter-annual variability in crop production, including both variations in crop yield and cropped area. The Ganges basin is used as a case study. First, we assessed the contribution of cropped area variability to overall variability in rice and wheat production by applying hierarchical partitioning on time-series of agricultural statistics. We then introduced cropped area as an endogenous decision variable in a hydro-economic optimization model (WaterWise), coupled to a hydrology-vegetation model (LPJmL), and analyzed to what extent its performance in the estimation of inter-annual variability in crop production improved. From the statistics, we found that in the period 1999–2009 seasonal adjustment in cropped area can explain almost 50% of variability in wheat production and 40% of variability in rice production in the Indian part of the Ganges basin. Our improved model was well capable of mimicking existing variability at different spatial aggregation levels, especially for wheat. The value of flexibility, i.e. the foregone costs of choosing not to crop in years when water is scarce, was quantified at 4% of gross margin of wheat in the Indian part of the Ganges basin and as high as 34% of gross margin of wheat in the drought-prone state of Rajasthan. We argue that flexibility in land use is an important coping strategy to rainfall variability in water stressed regions. [ABSTRACT FROM AUTHOR]

Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/. [ABSTRACT FROM AUTHOR]

Cotton is the main source of renewable fiber in the world and is primarily used for textile production. This study analyzed 3,487 papers on cotton research published during 1990 to 2021 from the agronomy category of the Web of Science (WoS). Papers were mainly written in English (96.329%), from 8,860 authors, 87 countries/territories, 1,661 organizations, and published in 107 journals and book series. The top five core journals were Crop Science (433, 12.418%), Agronomy Journal (310, 8.89%), Weed Technology (205, 5.879%), Field Crops Research (203, 5.822%), and Euphytica (189, 5.42%) with each publishing more than 189 papers. The top five countries and regions were the U.S., Peoples Republic of China, India, Brazil, and Australia. The top five organizations were USDA ARS, Texas A&M Univ., North Carolina State Univ., Univ. Georgia, and Cotton Incorporated. The top five authors were Johnie N. Jenkins, Don C. Jones, Jack C. McCarty, Jr., Jinfa Zhang, and C. Wayne Smith, with each publishing 72 papers or more. Visualizations using VOSviewer were conducted on WoS data to determine co-occurrence and clusters of connected publications, country input, organizations, and author collaboration (coauthorship) as well as clusters of all keywords of interrelated research topics. Based on the analysis of the network map of VOSviewer, there is cooperation among authors, organizations, and countries or regions. All keywords of the cotton research papers published during 1990 to 2021 from WoS agronomy category separated into six clusters based on different research topics. [ABSTRACT FROM AUTHOR]

Copyright of Journal of African Studies is the property of Japan Association for African Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

High-fat diet is associated with hypercholesterolemia and seminal alterations in White New Zealand rabbits. We have previously reported disorders in the development of the manchette-acrosome complex during spermiogenesis and decreased testicular efficiency in hypercholesterolemic rabbits. On the other hand, olive oil incorporated into the diet improves cholesterolemia and semen parameters affected in hypercholesterolemic rabbits. In this paper, we report the recovery—with the addition of olive oil to diet—from the sub-cellular mechanisms involved in the shaping of the sperm cell and testicular efficiency altered in hypercholesterolemic rabbits. Using morphological (structural, ultra-structural and immuno-fluorescence techniques) and cell biology techniques, a reorganization of the manchette and related structures was observed when olive oil was added to the high-fat diet. Specifically, actin filaments, microtubules and lipid rafts—abnormally distributed in hypercholesterolemic rabbits—were recovered with dietary olive oil supplementation. The causes of the decline in sperm count were studied in the previous report and here in more detail. These were attributed to the decrease in the efficiency index and also to the increase in the apoptotic percentage in testis from animals under the high-fat diet. Surprisingly, the addition of olive oil to the diet avoided the sub-cellular, efficiency and apoptosis changes observed in hypercholesterolemic rabbits. This paper reports the positive effects of the olive oil addition to the diet in the recovery of testicular efficiency and normal sperm shaping, mechanisms altered by hypercholesterolemia. [ABSTRACT FROM AUTHOR]

It is widely recognized that increasing agricultural production to the levels needed to feed an expanding world population requires sharply increased public investment in research and development and widespread adoption of new technologies, but funding for national and international agricultural research has rather declined in recent years. In this situation, priority setting has become increasingly important for allocating scarce research resources among competing needs to achieve greater impacts. Using partial equilibrium economic surplus models and poverty impact simulations, this paper assesses cassava research priorities in Africa, Latin America and Caribbean, and Asia based on the potential economic and poverty reduction impacts of alternative research and technology options. The results showed that efficient planting material production and distribution systems and sustainable crop and soil fertility management practices have the greatest expected economic and poverty reduction impacts in the three regions. Lack of clean planting materials is a major constraint to adoption and it is envisaged that efficient production and distribution systems for planting material can accelerate technology adoption by farmers. Similarly, sustainable crop and soil fertility management practices play a key role in closing the observed yield gaps, especially in Africa. The paper discusses the results of the priority assessment for key cassava research options and concludes with the implications for cassava research priorities. [ABSTRACT FROM AUTHOR]

In this paper we report on an automated procedure to capture and characterize the detailed structure of a crop canopy by means of stereo imaging. We focus attention specifically on the detailed characteristic of canopy height distribution—canopy shoot area as a function of height—which can provide an elaborate picture of canopy growth and health under a given set of conditions. We apply the method to a wheat field trial involving ten Australian wheat varieties that were subjected to two different fertilizer treatments. A novel camera self-calibration approach is proposed which allows the determination of quantitative plant canopy height data (as well as other valuable phenotypic information) by stereo matching. Utilizing the canopy height distribution to provide a measure of canopy height, the results compare favourably with manual measurements of canopy height (resulting in an R2 value of 0.92), and are indeed shown to be more consistent. By comparing canopy height distributions of different varieties and different treatments, the methodology shows that different varieties subjected to the same treatment, and the same variety subjected to different treatments can respond in much more distinctive and quantifiable ways within their respective canopies than can be captured by a simple trait measure such as overall canopy height. [ABSTRACT FROM AUTHOR]

Northern Kazakhstan is the main grain-producing region in the Republic of Kazakhstan. This paper presents a brief characteristic of soil climatic conditions and the results of long-term studies (n=600) on the diagnostics and optimization of the conditions of crops' nitrogen nutrition under insufficient and unstable moistening, which is typical for dryland Northern Kazakhstan. The methodological approach to the evaluation of the soil nitrogen state and the supply of nitrogen to crops were explicated. A new method of determining the need for nitrogen fertilizers and calculating their dose calculation was offered. The method was based on identification of the main factors of soil fertility that determine the efficiency of crops and the effectiveness of nitrogen fertilizers. On the basis of the correlation analysis, the quantitative correlation between the factors and the efficiency of crops was determined. The optimal parameters of nitrogen content in the soil, which provide for the maximum efficiency of crops, and ways of achieving such parameters were presented. The developed method allowed purposefully managing the soil nitrogen regime, with a view to optimizing nutrition and realizing the full potential of crops. [ABSTRACT FROM AUTHOR]

The paper retraces the GRASPgfs endeavor (Geospatial Resource for Agricultural Species and Pests with integrated workflow modelling to support Global Food Security) between multiple disciplines around a common objective of facilitating research and model simulations for sustainable food security. Within this endeavor, the geospatial media has been the enabler for multidisciplinary research in crop modelling. Geospatial genetic-trait variations and associations with environmental forecasting were the main focus of the GRASPgfs. Designing the platform achieving this objective generated a transdisciplinary vision of modelling and forecasting for sustainable agriculture. Based on interoperability principles, seamless access as well as sharing for data, metadata and processing models, the design is described in this paper. This geospatial binding facilitates and supports new types of hypotheses and analysis as illustrated in the paper with a landscape genetic case study (bambara groundnut) and a crop disease modelling (eyespot disease). The approach and the eGRASP platform are generic enough to accommodate further complexity into the integrated modelling that this geospatial binding enables. [ABSTRACT FROM AUTHOR]

The prediction of phenotypic traits using high-density genomic data has many applications such as the selection of plants and animals of commercial interest; and it is expected to play an increasing role in medical diagnostics. Statistical models used for this task are usually tested using cross-validation, which implicitly assumes that new individuals (whose phenotypes we would like to predict) originate from the same population the genomic prediction model is trained on. In this paper we propose an approach based on clustering and resampling to investigate the effect of increasing genetic distance between training and target populations when predicting quantitative traits. This is important for plant and animal genetics, where genomic selection programs rely on the precision of predictions in future rounds of breeding. Therefore, estimating how quickly predictive accuracy decays is important in deciding which training population to use and how often the model has to be recalibrated. We find that the correlation between true and predicted values decays approximately linearly with respect to either FST or mean kinship between the training and the target populations. We illustrate this relationship using simulations and a collection of data sets from mice, wheat and human genetics. [ABSTRACT FROM AUTHOR]

Senna alata, a flowering shrub, is widely cultivated in the Philippines for its anti-fungal properties. Despite this, its chloroplast genome is not yet established. We assembled and annotated the complete chloroplast genome of accession from the germplasm collection of the Institute of Crop Science, University of the Philippines, Los Baños, using Illumina sequencing data. The complete cp genome was 159,176-bp long characterized by a large single copy of 88,769 bp, short single-copy of 18,301 bp and a pair of inverted repeat regions of 26,053 bp each. The overall GC content of the chloroplast genome was 36.4%. The plastome comprised 37 tRNA genes, 8 rRNA genes and 78 mRNA genes. Phylogenetic analysis showed that S. alata is closely related to S. siamea. [ABSTRACT FROM AUTHOR]