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To improve the effect of sustainable agricultural development, this paper reports on sustainable agricultural development with the support of big data technology, combines the Internet of Things technology to apply blockchain in sustainable agricultural development, and analyzes the economic effect model. Moreover, this paper proves that the introduction of third‐party logistics in the vendor‐managed inventory strategy can effectively reduce the total cost of the supply chain. Through model analysis and model transformation, the inventory transportation integration optimization problem model is decomposed into two sub‐models: the transportation scheduling model and the replenishment delivery model. In addition, this paper designs a new solution algorithm based on the genetic algorithm and builds an intelligent blockchain model. The experimental research shows that the blockchain technology based on big data analysis proposed in this paper has certain effects. Core Ideas: Introduction of third‐party logistics in the VMI strategy can effectively reduce the total cost of the supply chain.Through model analysis and model transformation, the inventory transportation integration optimization problem model is decomposed into two sub‐models.The two sub‐models are the transportation scheduling model and the replenishment delivery model. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of agricultural product logistics transportation, based on big data technology, this paper starts from the factors that affect the efficiency of agricultural product logistics and combines big data technology to conduct the research on the mechanism and efficiency of agricultural products logistics transportation. Moreover, this paper analyzes how to improve the efficiency of agricultural product logistics by studying the dynamic relationship between influencing factors and the efficiency of agricultural product logistics. At the same time, this paper takes the efficiency evaluation of agricultural product logistics transportation network as the research object to improve the agricultural product data analysis algorithm, and constructs the system model from the perspective of the supply chain. In addition, this paper constructs system function modules according to the actual needs of agricultural product logistics transportation, and analyzes agricultural product transportation logistics by factor analysis. Finally, this paper verifies the effect of the model proposed in this paper through experimental research. From the test results, it can be seen that the model proposed in this paper basically meets the actual needs. Core Ideas: Agricultural product logistics are efficient and combine big data technology.The mechanism of agricultural products logistics transportation is efficient.Improve the efficiency of agricultural product logistics by studying the dynamic relationship between influencing. [ABSTRACT FROM AUTHOR]

Copyright of Agronomy Journal is the property of Wiley-Blackwell 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.)

Rectangular quadrats (1 m2 or less) are commonly used in cover crop research to sample shoot biomass in fields where the cover crops are planted in narrow rows. However, in drilled cover crops, quadrats do not accurately estimate cover crop biomass unless the width dimension of the quadrat, that runs perpendicular to the rows, is a multiple of the cover crop row spacing. Illustrations are provided with correctly sized and placed quadrats for sampling drilled cover crops along with calculations to convert sampled biomass to field‐scale units. Examples with various quadrat sizes, row spacings, and quadrat placements are used to illustrate how researchers can inadvertently underestimate or overestimate biomass by up to 49% and 27%, respectively. A survey of 92 articles with cover crop biomass data published in the Agronomy Journal and Weed Technology from 2017 to 2021 found that many studies used an incorrect quadrat size, which suggests that they may have inaccurately estimated cover crop biomass. However, assumptions were made to reach this conclusion because only a few papers provided the dimensions, orientation, and shape of the quadrat used. These results (1) raise important questions about possible errors made in papers reporting cover crop biomass data and associated metrics (nitrogen uptake, weed biomass, etc.), and (2) highlight the need for describing sampling methods in greater detail to ensure that accurate data and analysis are published in the literature. Approaches to correct possible errors in published research are described along with biomass sampling suggestions for future cover crop research. Core Ideas: Misuse of quadrats to sample drilled cover crops can inadvertently lead to inaccurate biomass estimates.A survey of recent literature suggests that quadrat misuse may have occurred in many drilled cover crop studies.Quadrat width should be a multiple of row spacing and quadrats should not be oriented diagonally to the rows.In broadcast cover crops, quadrat width and placement are irrelevant because plants are randomly scattered.Suggestions are made to avoid quadrat problems in future, drilled cover crop research and to correct past errors. [ABSTRACT FROM AUTHOR]

To provide agricultural tourism tourists with services that meet their various needs, it is necessary to fully clarify the information needs of tourists and achieve efficient management. The prerequisite for achieving efficient management requires fully grasping the influencing factors of tourists' various information behaviors. In order to improve the effect of agricultural tourism recommendation, this paper improves the agricultural intelligent tourism recommendation algorithm, and constructs the data processing module in combination with the intelligent network. Moreover, this paper constructs the system function structure according to actual needs, and analyzes the effect of the system through experimental research. From the experimental research, it can be seen that the agricultural intelligent tourism recommendation system based on the smart network constructed in this paper has a good recommendation effect, the response is very good among customers, and the customer satisfaction is high. Core Ideas: To improve the effect of agricultural tourism recommendation.To improves the agricultural intelligent tourism recommendation algorithm.To constructs the data processing module in combination with the intelligent network. [ABSTRACT FROM AUTHOR]

The year 2022 marks the ten‐year anniversary of the White House's Big Data Research and Development Initiative. While this initiative, and the others it spawned, helped to advance the many facets of data intensive research and discovery, obstacles and challenges still exist. If left unaddressed these obstacles will persist and at a minimum limit the potential of what can be achieved by harnessing the many new ways to collect, analyze, and share data and the insights that can be drawn from them. The opportunities and challenges related to Big Data in agriculture touch on all aspects of the general research data lifecycle; from instruments used to gather data, to advanced digital platforms used to store, analyze, and share data, and the innovative insights from using advanced computational methods. The eight papers included in this special issue were chosen in part because they highlight both the challenges and the opportunities that come from all stages of the data lifecycle common across agricultural research and development. These papers grew out of several workshops made possible by the support of the Midwest Regional Big Data Hub, which is sponsored by the National Science Foundation. Core Ideas: New data generators, data platforms, and analytical methods make the promises of Big Data as relevant as ever.Data intensive agricultural Research & Development is raising ownership and privacy concerns, especially by private stakeholders.Sustained agricultural innovation requires a pipeline of new solutions to address data ownership and privacy issues. [ABSTRACT FROM AUTHOR]

Climate change, land degradation, and limited land and water resources have challenged our ability to meet the food demand of a rapidly growing population. To tackle this challenge, modern agricultural systems are relying on new technologies like the Internet of Things (IoT) to improve agricultural productivity and resource use efficiency. Although IoT has gained considerable attention in the last few years, the key concepts of IoT and their applicability across different domains of agriculture are still new to many researchers, practitioners, managers, and policymakers. In this paper, we provide a comprehensive review of the use of different IoT platforms, wireless sensor networks, and other associated technologies like remote sensing, cloud computing, and big data analytics in digital agriculture. The review also explores the use of communication technologies, microcontrollers, and machine learning in smart irrigation and decision support systems. The necessity of interoperability (data transfer and communication without human interference) among devices is discussed in detail with regard to facilitating and exchanging agricultural data more effectively. The discussion also includes opportunities and challenges in standardizing IoT; developing energy‐efficient and affordable technologies; and improving data collection, transfer, storage, processing, security, anonymity, and privacy. This paper further calls for collaborative research in sensor and communication technologies in the agricultural sector. Core Ideas: Different platforms, sensors, and structures of IoT in digital agriculture were reviewed.The application of IoT in data management, interoperability, and precision farming was reviewed.Potential of IoT to improve agricultural quality and productivity was highlighted.Challenges and opportunities of IoT in digital agriculture were discussed. [ABSTRACT FROM AUTHOR]

The development of the Internet of Things (IoT) and machine learning (ML) technologies has triggered smart agricultural systems. In smart agriculture, irrigation management plays a major role to reduce water waste. The monitoring settings, hardware modules, communication technology, and storage systems used in smart irrigation systems were analyzed to determine the optimal nature of water flow. This assessment aims to give an overview of the current state of the irrigation system by taking into account weather and soil moisture. This paper provides a comprehensive review of the utilization of various hardware modules in smart irrigation systems. Moreover, various communication technologies that aid in data transfer for efficient smart irrigation are reviewed. The ML method used for prediction is also evaluated, as well as based storage technologies like the cloud and databases used to store data for predictive irrigation systems. As a result, the paper provides an overview of all the factors that contribute to irrigation systems' smart operation as well as potential future paths for improving agricultural systems through IoT. Core Ideas: Irrigation management is crucial in smart agriculture for minimizing water waste.This apper presents the ideal type of water flow that was determined by smart irrigation systems.Monitoring configurations, hardware modules, communication technology, and storage systems were analyzed in the smart irrigation system. [ABSTRACT FROM AUTHOR]

The 6th International Conference on Agricultural and Biological Sciences (ABS 2020), was planned to be held in the People's Republic of China. Due to the COVID-19 pandemic, ABS 2020, "Agricultural and Biological Sciences: Plant, Soil, Animal, and Environment", was the first online conference in Agricultural and Biological Sciences history. Each paper addressed a slightly different topic and provided identifiable challenges and research key questions in agriculture, agronomy, food production and security, and environmental hazards. The role of Agricultural and Biological Sciences of China is the generation of research knowledge that influence everyday activities. There were 41 manuscripts submitted, of which 25 were accepted for publication. The research domains varied and included the role of specific plant on soil C-cycling, haploid induction, and natural doubling of Zea mays L.; plant species and soil rhizosphere microflora; forest tree biomass succession and dynamics relevant to C-sequestration; making sandy land agriculture friendly; plantation age on C, N, and P stoichiometry; codon uses pattern of Gnetum luofuoense C.Y. Cheng using transcriptome data; suitability of plant landscape unit and natural parks; regeneration protocol of Jatropha curcas L., etc. The purpose of this special section is to generate an improved communication among international scientists that we hope will lead to enhanced food security. [ABSTRACT FROM AUTHOR]

Copyright of Agronomy Journal is the property of Wiley-Blackwell 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.)

Information about topics discussed at the American Society of Agronomy's (ASA) ACS324.1 "Journal of Environmental Quality" (JEQ) Editorial Board meeting held at the Hilton Palacio del Rio, La Condesa in San Antonio, Texas on October 19, 2011 is presented. Topics include the release of the mobile application SoilCropAgronPubs, JEQ expense and revenues and status of manuscripts. Attendees include ASA editor-in-chief Warren Dick, editor Denis Corwin and managing editor Mark Mandelbaum.

Soil acidification is an important cause of declining crop yields in many countries, including Canada and the United States. Meanwhile, alkaline by‐products from forest resources are widely available but underused in agriculture despite their expected benefits on soil pH and fertility. The aim of this study was to determine the effects, throughout a 40‐wk laboratory incubation, of six different forest‐derived liming materials on soil pH and Mehlich‐3–extractable major nutrients in two acidic soils. Lime mud, two wood ashes (papermill biosolids and wood bark), two biochars (maple and pine), and a de‐inking paper sludge (DPS) were applied at calcium carbonate equivalence–based rates, according to the amount of lime required to achieve a target pH of 6.5 on each soil. A calcitic lime (CL) was used as a reference. All forest‐derived materials except pine biochar were equally effective as CL in increasing the pH of the two acidic soils after 40 wk of incubation. Lime mud quickly raised the pH after soil incorporation, and then the pH progressively declined. By contrast, DPS upon decomposition gradually increased soil pH over time. In terms of liming value based on dry mass of each material, lime mud was needed at the lowest amount (0.8 CL unit) to increase pH to the target value. Wood ash, particularly from wood combustion, was a significant direct source of P, K, and Mg, whereas maple biochar supplied large amounts of available K and Mg. This study demonstrated that forest‐derived alkaline by‐products can efficiently remediate soil acidity and improve soil fertility. [ABSTRACT FROM AUTHOR]

This paper applies agricultural big data into agricultural economics management in order to improve agricultural economics management effects and comprehensively analyzes criterion and trending, maturity and advancement, practice and advancement, usability and extension of the platform according to the structure of agricultural economics management system by using big data. Meanwhile the paper describes the actual application condition of agricultural economics management, designs tests to analyze the agricultural economics data and digs the effects according to the agricultural economics management data through the crawler technology, and explains the application effects in the agricultural economics management. Tests and studies prove the reliability of the method put in the paper. Core Ideas: The system structure chart classifies the decision‐making system according to its functions.The main body of the data anomaly inspection model is data.Data duplication mostly occurs in general information systems. [ABSTRACT FROM AUTHOR]

Potassium (K) is an essential macronutrient that determines crop productivity and promotes crop growth under conditions of abiotic stress. In this review paper, peer‐reviewed publications (experimental data) dealing with the role of K administration in the control of cereals growth under drought stress are reviewed and analyzed. Most published research on the impact of K administration on cereals growth under drought focuses primarily on bread wheat (Triticum aestivum L.) and maize (Zea mays L.), along with barley (Hordeum vulgare L.), rice (Oryza sativa L.), pearl millet (Pennisetum glaucum L.), and sorghum (Sorghum bicolor L.). The beneficial effect of K on cereals growth under drought has been related to maintenance of water balance in the crops, increased activity of the antioxidant enzymes, increased synthesis of osmolytes, contribution to a low sodium (Na)/K ratio, extension of the grain filling period, and increase of the grain growth rate and weight. Moreover, K was suggested as a possible way for enhancing nitrogen use efficiency (e.g., bread wheat and sorghum). Nevertheless, the beneficial effect of foliar applied K in dried soil during grain filling differed among bread wheat cultivars, highlighting that the effect of K supply on grain weight may differ depending on application method, that is, administration through the soil or the foliage. Overall, K supply could be exploited as a tool for increasing cereal tolerance to drought, for example, by considering late administration of K through top dressing applications or foliar sprayings. Yet additional information is needed on the rate and the K form, along with the most suitable growth stage for application. Such information could help agronomists develop strategies for high‐quality cereal production in stressful environments. Core Ideas: Administration of K for improving cereals growth under drought has been studied primarily on bread wheat and maize.Most studies on cereals growth under drought refer to K administration trials in controlled environment.The benefits of K on cereals growth under drought have been related primarily to maintenance of water balance.K administration could be exploited as a potential tool for improving cereals tolerance to drought stress.Information on the K rate and form, along with the most suitable growth stage for application is needed. [ABSTRACT FROM AUTHOR]

Preharvest yield estimates can be used for harvest planning, marketing, and prescribing in‐season fertilizer and pesticide applications. One approach that is being widely tested is the use of machine learning (ML) or artificial intelligence (AI) algorithms to estimate yields. However, one barrier to the adoption of this approach is that ML/AI algorithms behave as a black block. An alternative approach is to create an algorithm using Bayesian statistics. In Bayesian statistics, prior information is used to help create the algorithm. However, algorithms based on Bayesian statistics are not often computationally efficient. The objective of the current study was to compare the accuracy and computational efficiency of four Bayesian models that used different assumptions to reduce the execution time. In this paper, the Bayesian multiple linear regression (BLR), Bayesian spatial, Bayesian skewed spatial regression, and the Bayesian nearest neighbor Gaussian process (NNGP) models were compared with ML non‐Bayesian random forest model. In this analysis, soybean (Glycine max) yields were the response variable (y), and spaced‐based blue, green, red, and near‐infrared reflectance that was measured with the PlanetScope satellite were the predictor (x). Among the models tested, the Bayesian (NNGP; R2‐testing = 0.485) model, which captures the short‐range correlation, outperformed the (BLR; R2‐testing = 0.02), Bayesian spatial regression (SRM; R2‐testing = 0.087), and Bayesian skewed spatial regression (sSRM; R2‐testing = 0.236) models. However, associated with improved accuracy was an increase in run time from 534 s for the BLR model to 2047 s for the NNGP model. These data show that relatively accurate within‐field yield estimates can be obtained without sacrificing computational efficiency and that the coefficients have biological meaning. However, all Bayesian models had lower R2 values and higher execution times than the random forest model. Core Ideas: Remote sensing‐based predictive models can estimate in‐season soybean yields.Among the Bayesian models tested, accepting simplifying assumptions reduced the execution times and R2 values.All Bayesian models had lower R2 values and higher execution times than the non‐Bayesian random forest model.Of the regression models, nearest neighbor Gaussian process has the highest R2 for training and testing data set.Model is transferable from one growing season to the next. [ABSTRACT FROM AUTHOR]

Data privacy has become a critical issue within the agriculture and food industry. The real‐time conversion of data to information has been shown to be incredibly valuable to the industry but often requires sharing data with software, platforms, customers, and regulators outside the data owner's control. While much work in this area has focused on legal protections for data privacy, less has been devoted to technical architectures to support different sharing models. This paper defines some "tools in the toolbox" for designing such systems that are accessible to both technical and nontechnical audiences as well as several "sharing design patterns" using the Open Ag Data Alliance (OADA) framework. These tools and patterns are helpful in classifying and understanding both existing and future data flows in agriculture and their privacy implications. Core Ideas: There are several composable, reusable concepts for designing privacy‐focused systems in agriculture.Data privacy concepts can be deployed via the OADA framework.Clear privacy design patterns exist for various types of use cases in agriculture.Standardization of privacy components across systems provides useful advanced patterns. [ABSTRACT FROM AUTHOR]

Information on the meeting of the editorial board of the "Journal of Environmental Quality" (JEQ) on November 2, 2010 in Long Beach, California is presented. It highlights the improvement in the times of handling manuscripts and the newly appointed associated editors (AEs) including Josh McGrath and Conrad Heaterwole. Moreover, it presents a summary of papers published for 2010 issue of JEQ

Copyright of Agronomy Journal is the property of Wiley-Blackwell 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.)

Copyright of Agronomy Journal is the property of Wiley-Blackwell 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.)

Copyright of Agronomy Journal is the property of Wiley-Blackwell 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.)

According to the current scenario, water crises are booming at a higher rate globally, affecting different demands such as agricultural, domestic, industrial, and so forth. Ultimately, such a water crisis may affect food safety. We have developed guar gum (GG)‐based hydrogel to help solve this issue with a water‐saving objective. This paper presents the application performance of newly developed GG‐based hydrogel on sugarcane crop growth, yield, and its economics. The crop water requirement and irrigation scheduling are also obtained using the CROPWAT model. Comparing different irrigation practices indicates that six irrigations can be saved by applying the CROPWAT optimization model, and up to three irrigations can be saved by applying different dosages of GG‐based hydrogel against the conventional method. Moreover, a set dosage application of 2.5 and 5 kg ha−1 of GG‐based hydrogel has elevated the cane height up to 8.67% and 10.02%, respectively. Furthermore, a considerable increment of 5.5% and 8% was found in the crop yield. In addition, the average soil moisture was found to be 10.50% and 17% higher over the field without hydrogel by using an indigenously developed Internet of Things–based SS316L soil moisture sensor. Applying GG‐based hydrogel (2.5 and 5 kg ha−1) saves the number of irrigations by two and three times, respectively. When it comes to the economy, the benefit–cost ratio for 2.5 and 5 kg ha−1 hydrogel dosages was found to be 2.64 and 2.09, respectively. Thus, GG‐based hydrogel shows substantial promise as a useful agricultural product for water‐saving purposes. Core Ideas: Optimizing the number of irrigation and irrigation scheduling with the CROPWAT model.Application of self‐developed GG‐based hydrogel to the sugarcane field.The average soil moisture was 10.50% and 17% higher than without hydrogel using a developed IoT‐based SS316L devise.Increased crop yield by 5.5%–8%, and reduced irrigation frequency to 12%–15% in water consumption.Economic assessment of sugarcane cane crops has been performed by various parameters. [ABSTRACT FROM AUTHOR]