Agriculture

30 pages, 2563 KB

Open AccessEditor’s ChoiceArticle

Reconnecting Farmers with Nature through Agroecological Transitions: Interacting Niches and Experimentation and the Role of Agricultural Knowledge and Innovation Systems

by Cynthia Giagnocavo, Miguel de Cara-García, Mónica González, Melchor Juan, José Ignacio Marín-Guirao, Sepide Mehrabi, Estefanía Rodríguez, Jan van der Blom and Eduardo Crisol-Martínez

Agriculture 2022, 12(2), 137; https://doi.org/10.3390/agriculture12020137 - 20 Jan 2022

Cited by 47 | Viewed by 10904

Abstract

Sustainability transitions in agriculture are explored through an analysis of niche initiatives within a common production system, relying on sustainable transitions, multi-level perspectives, and agroecological frameworks, and involving multi-actor, agricultural knowledge, and innovation systems (AKIS). The article focuses on how experimental niches and [...] Read more.

Sustainability transitions in agriculture are explored through an analysis of niche initiatives within a common production system, relying on sustainable transitions, multi-level perspectives, and agroecological frameworks, and involving multi-actor, agricultural knowledge, and innovation systems (AKIS). The article focuses on how experimental niches and sustainable activities affect farmers’ relationships with nature, and the reconceptualisation of the production system in which they operate, particularly where this system is embedded in less sustainable conventional or dominant regimes and landscapes. The need for fundamental changes, in the way that humans interact with nature, is widely argued for in order to achieve sustainable development, and farmers occupy a central role through participation in complex networks of agri-food systems. They have also found themselves disconnected from nature through conventional agri-industrial production practices. Four niches (biological control, ecological restoration, soil health, and ecological pond management) within the greenhouse sector of Almeria (SE Spain) are explored in a case study. Our results indicate that a farmer’s interaction with nature is functional, but through agroecological practices, a deeper understanding of the ecosystems in which greenhouse landscapes are embedded may be gained. As they become more connected to nature and benefit from ecosystem services, they can transition to more sustainable agricultural systems. Full article

(This article belongs to the Special Issue Reconnecting People with Nature through Agriculture)

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15 pages, 883 KB

Open AccessEditor’s ChoiceArticle

Effects of Adjuvants on Spraying Characteristics and Control Efficacy in Unmanned Aerial Application

by Shilin Wang, Xue Li, Aijun Zeng, Jianli Song, Tao Xu, Xiaolan Lv and Xiongkui He

Agriculture 2022, 12(2), 138; https://doi.org/10.3390/agriculture12020138 - 20 Jan 2022

Cited by 36 | Viewed by 5929

Abstract

Pesticide application by unmanned agricultural aerial vehicles (UAVs) has rapidly developed in China and other Asian counties. Currently, tank-mix spray adjuvants are usually added into pesticide solutions to reduce spray drift and facilitate droplet deposition and control efficacy. The currently used tank-mix adjuvants [...] Read more.

Pesticide application by unmanned agricultural aerial vehicles (UAVs) has rapidly developed in China and other Asian counties. Currently, tank-mix spray adjuvants are usually added into pesticide solutions to reduce spray drift and facilitate droplet deposition and control efficacy. The currently used tank-mix adjuvants are all derived from conventional ground sprays, and their mechanisms of action in aerial applications are still unclear. In order to clarify the spraying characteristics and control efficacy of those adjuvants in aerial sprays, the performances of various types of tank-mix adjuvants were compared by analyzing droplet spectrum, drift potential index (DIX) in a wind tunnel, field deposition and control efficacy on wheat rust and aphids. The atomization results showed that the addition of adjuvants could change the droplet spectrum of liquid, and the results suggest that droplet size is an effective indicator of spray drift potential. In the field application, the meteorological conditions are complex and uncontrollable, and the effects of adjuvants on droplet deposition and distribution were not significant. Compared with the control solution, there was no significant difference in the deposition amount of each adjuvant solution, and the CVs of deposition were higher than 30%. Adding adjuvants to the spray solution can significantly improve the control efficacy of pesticides on wheat aphids and rust and also prolong the duration of the pesticide. Our results suggest that tank-mix adjuvants should be added when UAVs are used for aerial application. This study can be used as a reference to the research and development or selection of adjuvants in aerial sprays of UAVs. Full article

(This article belongs to the Special Issue Sustainable Use of Pesticides)

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17 pages, 97282 KB

Open AccessEditor’s ChoiceArticle

Artificial Intelligence-Based Real-Time Pineapple Quality Classification Using Acoustic Spectroscopy

by Ting-Wei Huang, Showkat Ahmad Bhat, Nen-Fu Huang, Chung-Ying Chang, Pin-Cheng Chan and Arnold R. Elepano

Agriculture 2022, 12(2), 129; https://doi.org/10.3390/agriculture12020129 - 18 Jan 2022

Cited by 25 | Viewed by 6749

Abstract

The pineapple is an essential fruit in Taiwan. Farmers separate pineapples into two types, according to the percentages of water in the pineapples. One is the “drum sound pineapple” and the other is the “meat sound pineapple”. As there is more water in [...] Read more.

The pineapple is an essential fruit in Taiwan. Farmers separate pineapples into two types, according to the percentages of water in the pineapples. One is the “drum sound pineapple” and the other is the “meat sound pineapple”. As there is more water in the meat sound pineapple, the meat sound pineapple more easily rots and is more challenging to store than the drum sound pineapple. Thus, farmers need to filter out the meat sound pineapple, so that they can sell pineapples overseas. The classification, based on striking the pineapple fruit with rigid objects (e.g., plastic rulers) is most commonly used by farmers due to the negligibly low costs and availability. However, it is a time-consuming job, so we propose a method to automatically classify pineapples in this work. Using embedded onboard computing processors, servo, and an ultrasonic sensor, we built a hitting machine and combined it with a conveyor to automatically separate pineapples. To classify pineapples, we proposed a method related to acoustic spectrogram spectroscopy, which uses acoustic data to generate spectrograms. In the acoustic data collection step, we used the hitting machine mentioned before and collected many groups of data with different factors; some groups also included the noise in the farm. With these differences, we tested our deep learning-based convolutional neural network (CNN) performances. The best accuracy of the developed CNN model is 0.97 for data Group V. The proposed hitting machine and the CNN model can assist in the classification of pineapple fruits with high accuracy and time efficiency. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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15 pages, 3244 KB

Open AccessEditor’s ChoiceArticle

Agro Climatic Zoning of Saffron Culture in Miyaneh City by Using WLC Method and Remote Sensing Data

by Ali Zamani, Alireza Sharifi, Shilan Felegari, Aqil Tariq and Na Zhao

Agriculture 2022, 12(1), 118; https://doi.org/10.3390/agriculture12010118 - 17 Jan 2022

Cited by 66 | Viewed by 5562

Abstract

Recent continuous droughts and decreasing ground water tables have prompted efforts to improve irrigation schedules and introduce crops that need less water. A study was recently conducted to determine suitable zones for saffron in Miyaneh using Landsat-8 images and the weighted linear combination [...] Read more.

Recent continuous droughts and decreasing ground water tables have prompted efforts to improve irrigation schedules and introduce crops that need less water. A study was recently conducted to determine suitable zones for saffron in Miyaneh using Landsat-8 images and the weighted linear combination (WLC) method. Climatic and geographical indices for saffron cultivation in the region were for soil type, slope, soil moisture, and soil salinity. Parameters such as 30 years of data on climate, soil, and water conditions were collected from synoptic and climatologic stations such as Tabriz. Then, parameters were weighted using WLC for importance in each region. The data were transferred to expert choice and clustered, rated, and integrated to produce the last layer. The results showed that the southeastern and northwestern regions of Miyaneh, especially the banks of rivers and catchments, were identified as suitable places for saffron cultivation and that 28% of the area is in the suitable class, 36% in the relatively moderately suitable class, 20% in the critical suitability class, and the rest of the area, which covers about 16% of the area, is in the non-suitable class. Therefore, if it is possible to identify favorable areas for saffron cultivation according to the climatic requirements and it is possible in practice to achieve higher performance per unit area, that in itself will contribute to improved economic conditions and levels of income for farmers. Due to the special characteristics of saffron, substituting it for the cultivation of crops with high water requirements, such as onions, potatoes, tomatoes, etc., will help reduce water consumption. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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16 pages, 4414 KB

Open AccessEditor’s ChoiceArticle

Combining Random Forest and XGBoost Methods in Detecting Early and Mid-Term Winter Wheat Stripe Rust Using Canopy Level Hyperspectral Measurements

by Linsheng Huang, Yong Liu, Wenjiang Huang, Yingying Dong, Huiqin Ma, Kang Wu and Anting Guo

Agriculture 2022, 12(1), 74; https://doi.org/10.3390/agriculture12010074 - 6 Jan 2022

Cited by 62 | Viewed by 5852

Abstract

Appropriate modeling methods and feature selection algorithms must be selected to improve the accuracy of early and mid-term remote sensing detection of wheat stripe rust. In the current study, we explored the effectiveness of the random forest (RF) algorithm combined with the extreme [...] Read more.

Appropriate modeling methods and feature selection algorithms must be selected to improve the accuracy of early and mid-term remote sensing detection of wheat stripe rust. In the current study, we explored the effectiveness of the random forest (RF) algorithm combined with the extreme gradient boosting (XGboost) method for early and mid-term wheat stripe rust detection based on the vegetation indices extracted from canopy level hyperspectral measurements. Initially, 21 vegetation indices that were related to the early and mid-term winter wheat stripe rust were calculated on the basis of canopy level hyperspectral reflectance. Subsequently, the optimal vegetation index combination for disease detection was determined using correlation analysis (CA) combined with RF algorithms. Then, the disease severity detection model of early and mid-term winter wheat stripe rust was constructed using XGBoost method based on the optimal vegetation index combination. For the evaluation and comparison of the initial results, three commonly used classification methods, namely, RF, backpropagation neural network (BPNN), and support vector machine (SVM), were utilized. The vegetation index combinations determined by the single CA algorithm were also used to construct detection models. Compared with the detection models based on the vegetation index combination obtained using the single CA algorithm, the overall accuracy of the four detection models based on the optimal vegetation index combination based on CA combined with RF algorithms increased by 16.1% (XGBoost), 9.7% (RF), 8.1% (SVM), and 8.1% (BPNN). Among the eight models, the XGBoost detection model based on the optimal vegetation index combination using CA combined with RF algorithms, CA-RF-XGBoost, achieved the highest overall accuracy of 87.1% and the highest kappa coefficient of 0.798. Our results indicate that the RF combined with XGBoost can improve the detection accuracy of early and mid-term winter wheat stripe rust effectively at canopy scale. Full article

(This article belongs to the Section Agricultural Technology)

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19 pages, 1384 KB

Open AccessEditor’s ChoiceArticle

Effects of the COVID-19 Pandemic on Sustainable Food Systems: Lessons Learned for Public Policies? The Case of Poland

by Michał Dudek and Ruta Śpiewak

Agriculture 2022, 12(1), 61; https://doi.org/10.3390/agriculture12010061 - 4 Jan 2022

Cited by 50 | Viewed by 6628

Abstract

COVID-19 has affected the functioning of food systems all over the world. This paper seeks to identify and analyse the economic, legal and institutional, as well as social effects of the pandemic’s outbreak on food systems, and the implications for the EU Farm [...] Read more.

COVID-19 has affected the functioning of food systems all over the world. This paper seeks to identify and analyse the economic, legal and institutional, as well as social effects of the pandemic’s outbreak on food systems, and the implications for the EU Farm to Fork Strategy whose main purpose is to put food systems on a sustainable path. Qualitative economic and social impact analysis was used to identify the above types of effect on the food system on a macroscale, using Poland as an example. Information was sourced from existing data and qualitative studies. Studies show that the consequences of the pandemic for individual elements of the food system in Poland in 2020 were related to numerous disruptions in functioning, leading to uncertainty, financial losses, and interrupted transactions. The crisis under analysis also revealed modifications in these actors’ behaviours in food markets, noticeable in changes in consumption patterns and in the ways demand for food was met. Nevertheless, an analysis of the gathered information and data testifies to the food system’s relative resistance to the effects of the pandemic, and also to the adaptive skills of the system’s entities, especially food producers and consumers. The paper’s discussion contains recommendations for public policies shaping the food system, pointing to actions that might reduce the negative effects of other potential exogenic crises in the future and aid the implementation of the Farm to Fork Strategy’s principles. Full article

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14 pages, 2907 KB

Open AccessEditor’s ChoiceArticle

Dietary Resveratrol Alleviates AFB1-Induced Ileum Damage in Ducks via the Nrf2 and NF-κB/NLRP3 Signaling Pathways and CYP1A1/2 Expressions

by Hao Yang, Yingjie Wang, Chunting Yu, Yihan Jiao, Ruoshi Zhang, Sanjun Jin and Xingjun Feng

Agriculture 2022, 12(1), 54; https://doi.org/10.3390/agriculture12010054 - 1 Jan 2022

Cited by 34 | Viewed by 3776

Abstract

The aim of this study was to explore the mechanism underlying the protective effects of resveratrol against Aflatoxin B1-induced ileum injury in ducks. A corn–soybean meal-basal diet and two test diets (500 mg/kg resveratrol +0.2 mg Aflatoxin B1/kg, 0.2 mg AFB₁/kg) [...] Read more.

The aim of this study was to explore the mechanism underlying the protective effects of resveratrol against Aflatoxin B1-induced ileum injury in ducks. A corn–soybean meal-basal diet and two test diets (500 mg/kg resveratrol +0.2 mg Aflatoxin B1/kg, 0.2 mg AFB₁/kg) were used in a 10-wk design trial (n = 15 ducks/group). These results showed that the toxicity of Aflatoxin B1 significantly reduced the antioxidant capacity of duck ileum and induced inflammation, oxidative stress, mitochondrial dysfunction and DNA damage in ducks. The expression of genes, including CYP1A2, CYP2A6, and CYP3A4, at the mRNA level was significantly upregulated (p < 0.05) by AFB₁. The level of Nrf2 was suppressed (p < 0.05) and the mRNA and protein level of NF-κB was activated (p < 0.05) in the AFB₁ group. However, supplementation with 500 mg/kg dietary resveratrol in Aflatoxin B1-induced ducks significantly ameliorated these alterations and decreased the mRNA expression of CYP1A1 and CYP1A2 (p < 0.05) and the production of AFB₁-DNA adducts (p < 0.05). The results proved that resveratrol alleviated ileum injury induced by AFB1, decreased the production of AFB₁-DNA adducts by downregulating the expression of CYP1A1 and CYP1A2, and reduced DNA damage and oxidative stress via the Nrf2/ Keap1 and NF-κB/NLRP3 signaling pathways. Full article

(This article belongs to the Special Issue New and Alternative Feeds, Additives, and Supplements)

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18 pages, 1032 KB

Open AccessEditor’s ChoiceReview

The Role of Insect Cytochrome P450s in Mediating Insecticide Resistance

by Min Ye, Bidhan Nayak, Lei Xiong, Chao Xie, Yi Dong, Minsheng You, Zhiguang Yuchi and Shijun You

Agriculture 2022, 12(1), 53; https://doi.org/10.3390/agriculture12010053 - 1 Jan 2022

Cited by 58 | Viewed by 12709

Abstract

In many organisms, cytochrome P450 enzymes are the primary detoxifying enzymes. Enhanced P450 activity can be mediated by the emergence of new genes, increased transcription due to mutations in the promoter regions, changes in enzyme structures and functions due to mutations in protein-coding [...] Read more.

In many organisms, cytochrome P450 enzymes are the primary detoxifying enzymes. Enhanced P450 activity can be mediated by the emergence of new genes, increased transcription due to mutations in the promoter regions, changes in enzyme structures and functions due to mutations in protein-coding regions, or changes in post-translational modifications; all of these changes are subject to insecticide selection pressure. Multiple signalling pathways and key effector molecules are involved in the regulation of insect P450s. Increased P450 activity is a key mechanism inducing insect resistance. Hence, downregulation of selected P450s is a promising strategy to overcome this resistance. Insect P450 inhibitors that act as insecticide synergists, RNA interference to induce P450 gene silencing, and the use of transgenic insects and crops are examples of strategies utilized to overcome resistance. This article reviews the latest advances in studies related to insect P450s-mediated agrochemical resistance, with focuses on the regulatory mechanisms and associated pest management strategies. Future investigations on the comprehensive regulatory pathways of P450-mediated detoxification, identification of key effectors, and downregulation strategies for P450s will ecologically, economically, and practically improve pest management. Full article

(This article belongs to the Special Issue Sustainable Use of Pesticides)

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15 pages, 2682 KB

Open AccessEditor’s ChoiceArticle

Calibration and Tests for the Discrete Element Simulation Parameters of Fallen Jujube Fruit

by Gaokun Shi, Jingbin Li, Longpeng Ding, Zhiyuan Zhang, Huizhe Ding, Ning Li and Za Kan

Agriculture 2022, 12(1), 38; https://doi.org/10.3390/agriculture12010038 - 29 Dec 2021

Cited by 33 | Viewed by 3609

Abstract

Discrete element method (DEM) simulation is an important method to analyze the interaction relationship between materials and equipment, and to develop machinery and/or equipment. However, it is necessary to input specific simulation parameters when establishing a DEM simulation model. In this study, the [...] Read more.

Discrete element method (DEM) simulation is an important method to analyze the interaction relationship between materials and equipment, and to develop machinery and/or equipment. However, it is necessary to input specific simulation parameters when establishing a DEM simulation model. In this study, the interval values were measured through angle of repose tests of fallen jujube fruit (FJF), and the simulation angle of repose tests for FJF were established with EDEM software (DEM Solutions Ltd. Edinburgh, Scotland, UK). Then, the Plackett-Burman design, steepest ascent search experiment, and center composite design experimental methods were utilized to obtain the specific values of the simulation parameters from the interval values. The results showed that significant influencing factors in the simulation angle of repose include the Poisson’s ratio, the static friction coefficient between FJF, and the static friction coefficient between FJF and the steel plate, for which the optimal values were 0.248, 0.480, and 0.309, respectively. The angle of repose tests’ results showed that the error was 0.53% between the simulation angle of repose (29.69°) and the angle of repose (29.85°). In addition, the flow rate test results showed that the average error was 5.84% between the physical and simulation tests. This indicated that the calibrated parameters were accurate and reliable, and that the simulation model can accurately represent the physical tests. Consequently, this study provides an EDEM model of FJF that was essential in designing machinery and equipment through the EDEM simulation method. Full article

(This article belongs to the Special Issue Innovations and Practices of Agricultural Green Mechanized Production Technology)

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12 pages, 1001 KB

Open AccessEditor’s ChoiceArticle

The Competitiveness of Agriculture in EU Member States According to the Competitiveness Pyramid Model

by Anna Nowak and Monika Różańska-Boczula

Agriculture 2022, 12(1), 28; https://doi.org/10.3390/agriculture12010028 - 28 Dec 2021

Cited by 29 | Viewed by 5973

Abstract

Agriculture in the European Union is highly differentiated, and one of the objectives of the Common Agricultural Policy is to improve agricultural competitiveness. Therefore, surveys regarding the competitiveness of agriculture and grouping countries of the European Union (EU) according to similar characteristics of [...] Read more.

Agriculture in the European Union is highly differentiated, and one of the objectives of the Common Agricultural Policy is to improve agricultural competitiveness. Therefore, surveys regarding the competitiveness of agriculture and grouping countries of the European Union (EU) according to similar characteristics of agriculture are very valuable. They help make strategic decisions concerning the agricultural sector. This paper aims to evaluate the agricultural competitiveness of EU member states in 2010–2019. Data used is derived from the Eurostat and FADN (Farm Accountancy Data Network). The study employed a competitiveness pyramid model based on two groups of factors–competitiveness sources (bottom of the pyramid) and competitiveness effects. Partial components allocated to the groups mentioned above of factors were used to calculate a synthetic measure to determine the level of agricultural competitiveness in respective countries. The studies revealed that EU agriculture varies both in terms of resources and relationships between production factors, as well as the efficiency of their utilization. A clear difference in the level of competitiveness occurred between old and new member states, although some new countries ranked relatively high in terms of competitiveness sources (Czechia and Poland). Belgium scored highest for the synthetic measure of agricultural competitiveness in 2010–2019, and Cyprus had the lowest. It was demonstrated that human resources were of utmost importance in the structure of competitiveness sources. In turn, the average holding area determined the management conditions to the highest extent. Full article

(This article belongs to the Special Issue Agricultural Food Marketing, Economics and Policies)

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16 pages, 1682 KB

Open AccessEditor’s ChoiceArticle

Analysis of Green Total Factor Productivity of Grain and Its Dynamic Distribution: Evidence from Poyang Lake Basin, China

by Bingfei Bao, Shengtian Jin, Lilian Li, Kaifeng Duan and Xiaomei Gong

Agriculture 2022, 12(1), 8; https://doi.org/10.3390/agriculture12010008 - 22 Dec 2021

Cited by 16 | Viewed by 3542

Abstract

Based on the grain production data of the counties (cities, districts) in Poyang Lake Basin, this paper uses the productivity index of Epsilon Based Measure of Malmquist Luenberger (EBM-ML Index) to analyse the green total factor productivity (GTFP) of grain in Poyang Lake [...] Read more.

Based on the grain production data of the counties (cities, districts) in Poyang Lake Basin, this paper uses the productivity index of Epsilon Based Measure of Malmquist Luenberger (EBM-ML Index) to analyse the green total factor productivity (GTFP) of grain in Poyang Lake Basin. Kernel density function and Markov analysis are used to discuss the dynamic evolution process of the distribution of GTFP of grain. The results show the following: (1) From the time dimension, the GTFP of grain is on the rise and fluctuates more frequently from 2001 to 2017, and its trend of change is determined by the combination of technical efficiency and technological progress. Moreover, from a spatial dimension, the number of counties (cities, districts) with GTFP of grain greater than 1.0 has shown an overall increase, indicating that the overall level of GTFP of grain is increasing. (2) According to the kernel density estimation results, the crest of the main peak of the kernel density curve corresponding to the GTFP of grain in Poyang Lake Basin shifts to the right, and the area formed by the right part of the GTFP of grain corresponding to the crest of the main peak of its kernel density curve gradually increases. The peak of the kernel density curve changes from “multi-peak mode” to “single-peak mode,” and the height of the main peak of the kernel density curve of GTFP of grain shows an overall decrease. Meanwhile, the right tail of the kernel density curve shows an overall extending trend. (3) According to the estimation results of the Markov chain, the GTFP of grain in Poyang Lake Basin is highly mobile from 2001 to 2017, and the counties (cities, districts) have a certain degree of agglomeration in the low, medium-low, medium-high and high levels. In other words, the long-term equilibrium state of growth of GTFP of grain remains dispersed in the state space of four level types, indicating that the divergence state of GTFP of grain in counties (cities, districts) of Poyang Lake Basin will continue for a long time in the future. The study reveals the evolution and dynamic change of GTFP of grain in Poyang Lake Basin, which has important theoretical significance and practical value for optimizing the spatial pattern and realizing the balanced development of GTFP among counties (cities, districts) of Poyang Lake Basin and consolidating China’s food security strategy. Full article

(This article belongs to the Section Agricultural Economics, Policies and Rural Management)

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29 pages, 2248 KB

Open AccessFeature PaperEditor’s ChoiceReview

On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

by Houda Orchi, Mohamed Sadik and Mohammed Khaldoun

Agriculture 2022, 12(1), 9; https://doi.org/10.3390/agriculture12010009 - 22 Dec 2021

Cited by 120 | Viewed by 27886

Abstract

The agricultural sector remains a key contributor to the Moroccan economy, representing about 15% of gross domestic product (GDP). Disease attacks are constant threats to agriculture and cause heavy losses in the country’s economy. Therefore, early detection can mitigate the severity of diseases [...] Read more.

The agricultural sector remains a key contributor to the Moroccan economy, representing about 15% of gross domestic product (GDP). Disease attacks are constant threats to agriculture and cause heavy losses in the country’s economy. Therefore, early detection can mitigate the severity of diseases and protect crops. However, manual disease identification is both time-consuming and error prone, and requires a thorough knowledge of plant pathogens. Instead, automated methods save both time and effort. This paper presents a contemporary overview of research undertaken over the past decade in the field of disease identification of different crops using machine learning, deep learning, image processing techniques, the Internet of Things, and hyperspectral image analysis. Additionally, a comparative study of several techniques applied to crop disease detection was carried out. Furthermore, this paper discusses the different challenges to be overcome and possible solutions. Then, several suggestions to address these challenges are provided. Finally, this research provides a future perspective that promises to be a highly useful and valuable resource for researchers working in the field of crop disease detection. Full article

(This article belongs to the Special Issue Digital Innovations in Agriculture)

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16 pages, 4770 KB

Open AccessEditor’s ChoiceArticle

Application of Fourier Transform Infrared Spectroscopy and Multivariate Analysis Methods for the Non-Destructive Evaluation of Phenolics Compounds in Moringa Powder

by Rahul Joshi, Ramaraj Sathasivam, Sang Un Park, Hongseok Lee, Moon S. Kim, Insuck Baek and Byoung-Kwan Cho

Agriculture 2022, 12(1), 10; https://doi.org/10.3390/agriculture12010010 - 22 Dec 2021

Cited by 20 | Viewed by 6134

Abstract

This study performed non-destructive measurements of phenolic compounds in moringa powder using Fourier Transform Infrared (FT-IR) spectroscopy within a spectral range of 3500–700 cm⁻¹. Three major phenolic compounds, namely, kaempferol, benzoic acid, and rutin, were measured in five different varieties of [...] Read more.

This study performed non-destructive measurements of phenolic compounds in moringa powder using Fourier Transform Infrared (FT-IR) spectroscopy within a spectral range of 3500–700 cm⁻¹. Three major phenolic compounds, namely, kaempferol, benzoic acid, and rutin, were measured in five different varieties of moringa powder, which was approved with respect to the high-performance liquid chromatography (HPLC) method. The prediction performance of three different regression methods, i.e., partial least squares regression (PLSR), principal component regression (PCR), and net analyte signal (NAS)-based methodology, called hybrid linear analysis (HLA/GO), were compared to achieve the best prediction model. The obtained results for the PLS regression method resulted in better performance for the prediction analysis of phenolic compounds in moringa powder. The PLSR model attained a correlation coefficient (

R_{p}^{2}

) value of 0.997 and root mean square error of prediction (RMSEP) of 0.035 mg/g, respectively, which is comparatively higher than the other two regression models. Based on the results, it can be concluded that FT-IR spectroscopy in conjugation with a suitable regression analysis method could be an effective analytical tool for the non-destructive prediction of phenolic compounds in moringa powder. Full article

(This article belongs to the Special Issue Sensors Applied to Agricultural Products)

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12 pages, 49264 KB

Open AccessEditor’s ChoiceArticle

DigiPig: First Developments of an Automated Monitoring System for Body, Head and Tail Detection in Intensive Pig Farming

by Marko Ocepek, Anja Žnidar, Miha Lavrič, Dejan Škorjanc and Inger Lise Andersen

Agriculture 2022, 12(1), 2; https://doi.org/10.3390/agriculture12010002 - 21 Dec 2021

Cited by 22 | Viewed by 7073

Abstract

The goal of this study was to develop an automated monitoring system for the detection of pigs’ bodies, heads and tails. The aim in the first part of the study was to recognize individual pigs (in lying and standing positions) in groups and [...] Read more.

The goal of this study was to develop an automated monitoring system for the detection of pigs’ bodies, heads and tails. The aim in the first part of the study was to recognize individual pigs (in lying and standing positions) in groups and their body parts (head/ears, and tail) by using machine learning algorithms (feature pyramid network). In the second part of the study, the goal was to improve the detection of tail posture (tail straight and curled) during activity (standing/moving around) by the use of neural network analysis (YOLOv4). Our dataset (n = 583 images, 7579 pig posture) was annotated in Labelbox from 2D video recordings of groups (n = 12–15) of weaned pigs. The model recognized each individual pig’s body with a precision of 96% related to threshold intersection over union (IoU), whilst the precision for tails was 77% and for heads this was 66%, thereby already achieving human-level precision. The precision of pig detection in groups was the highest, while head and tail detection precision were lower. As the first study was relatively time-consuming, in the second part of the study, we performed a YOLOv4 neural network analysis using 30 annotated images of our dataset for detecting straight and curled tails. With this model, we were able to recognize tail postures with a high level of precision (90%). Full article

(This article belongs to the Special Issue Recent Advances in Livestock Production and Animal Welfare)

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9 pages, 1604 KB

Open AccessEditor’s ChoiceArticle

Field Inoculation of Arbuscular Mycorrhizal Fungi Improves Fruit Quality and Root Physiological Activity of Citrus

by Ming-Ao Cao, Peng Wang, Abeer Hashem, Stephan Wirth, Elsayed Fathi Abd_Allah and Qiang-Sheng Wu

Agriculture 2021, 11(12), 1297; https://doi.org/10.3390/agriculture11121297 - 20 Dec 2021

Cited by 28 | Viewed by 5854

Abstract

Soil arbuscular mycorrhizal (AM) fungi form a mutualistic symbiosis with plant roots and produce many benefits on host plants under potted conditions, while field inoculation of AM fungi on citrus (a woody plant) has been rarely reported. The present study aimed to analyze [...] Read more.

Soil arbuscular mycorrhizal (AM) fungi form a mutualistic symbiosis with plant roots and produce many benefits on host plants under potted conditions, while field inoculation of AM fungi on citrus (a woody plant) has been rarely reported. The present study aimed to analyze the changes in mycorrhizal growth, root vitality, and fruit quality of Citrus reticulata Blanco var. Ponkan mandarin cv. Jinshuigan grafted on Poncirus trifoliata L. after inoculation with a mix of AM fungi (Diversispora versiformis, Funneliformis mosseae, and Rhizophagus intraradices) and single F. mosseae. After the second year of AM fungal inoculations, root mycorrhizal colonization (%), root vitality, hyphal length in soil, and easily extractable glomalin-related soil protein content were significantly increased, while difficult-to-extract glomalin-related soil protein content was decreased. Two mycorrhizal fungal inoculation treatments collectively improved fruit quality parameters such as polar diameter, equatorial diameter, the weight of single fruits, fruit peel, and sarcocarp, coloration value, and soluble solids content. Our study, therefore, suggested that field inoculation with AM fungi improved root physiological activities in terms of mycorrhizal growth and root vitality and thus improved fruit quality. The effect of mixed-AM treatment was more significant than that of F. mosseae alone. Full article

(This article belongs to the Special Issue Advanced Research of Rhizosphere Microbial Activity)

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20 pages, 6394 KB

Open AccessEditor’s ChoiceArticle

Multiscale Assessments of Three Reanalysis Temperature Data Systems over China

by Xiaolong Huang, Shuai Han and Chunxiang Shi

Agriculture 2021, 11(12), 1292; https://doi.org/10.3390/agriculture11121292 - 19 Dec 2021

Cited by 35 | Viewed by 4265

Abstract

Temperature is one of the most important meteorological variables for global climate change and human sustainable development. It plays an important role in agroclimatic regionalization and crop production. To date, temperature data have come from a wide range of sources. A detailed understanding [...] Read more.

Temperature is one of the most important meteorological variables for global climate change and human sustainable development. It plays an important role in agroclimatic regionalization and crop production. To date, temperature data have come from a wide range of sources. A detailed understanding of the reliability and applicability of these data will help us to better carry out research in crop modelling, agricultural ecology and irrigation. In this study, temperature reanalysis products produced by the China Meteorological Administration Land Data Assimilation System (CLDAS), the U.S. Global Land Data Assimilation System (GLDAS) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version5 (ERA5)-Land are verified against hourly observations collected from 2265 national automatic weather stations (NAWS) in China for the period 2017–2019. The above three reanalysis systems are advanced and widely used multi-source data fusion and re-analysis systems at present. The station observations have gone through data Quality Control (QC) and are taken as “true values” in the present study. The three reanalysis temperature datasets were spatial interpolated using the bi-linear interpolation method to station locations at each time. By calculating the statistical metrics, the accuracy of the gridded datasets can be evaluated. The conclusions are as follows. (1) Based on the evaluation of temporal variability and spatial distribution as well as correlation and bias analysis, all the three reanalysis products are reasonable in China. (2) Statistically, the CLDAS product has the highest accuracy with the root mean square error (RMSE) of 0.83 °C. The RMSEs of the other two reanalysis datasets produced by ERA5-Land and GLDAS are 2.72 °C and 2.91 °C, respectively. This result indicates that the CLDAS performs better than ERA5-Land and GLDAS, while ERA5-Land performs better than GLDAS. (3) The accuracy of the data decreases with increasing elevation, which is common for all of the three products. This implies that more caution is needed when using the three reanalysis temperature data in mountainous regions with complex terrain. The major conclusion of this study is that the CLDAS product demonstrates a relatively high reliability, which is of great significance for the study of climate change and forcing crop models. Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

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20 pages, 340 KB

Open AccessFeature PaperEditor’s ChoiceArticle

AgriTech Innovators: A Study of Initial Adoption and Continued Use of a Mobile Digital Platform by Family-Operated Farming Enterprises

by Grace Fox, John Mooney, Pierangelo Rosati and Theo Lynn

Agriculture 2021, 11(12), 1283; https://doi.org/10.3390/agriculture11121283 - 16 Dec 2021

Cited by 25 | Viewed by 6477

Abstract

While information technology is playing a significant transformative role in virtually every industry, within the agriculture sector, family-operated farming enterprises have been slow to adopt IT solutions to manage their operations. This study adopts a sequential mixed-methods research design to examine the pre- [...] Read more.

While information technology is playing a significant transformative role in virtually every industry, within the agriculture sector, family-operated farming enterprises have been slow to adopt IT solutions to manage their operations. This study adopts a sequential mixed-methods research design to examine the pre- and post-adoption phases of farmers’ use of a mobile digital platform for farm management. Our findings show that farmers’ initial acceptance of a mobile digital platform for farm management is shaped by social influence, which mediates the impact of performance and effort expectancy. Post-adoption continued use of the digital platform is influenced directly by performance and effort expectancy and indirectly by trust beliefs and social influence. Perceived work impediment indirectly influences post-adoption acceptance via effort expectancy. Our study untangles the direct and indirect influences of positive and negative perceptions on farmers’ acceptance of a new innovative AgriTech digital platform in these different phases. Full article

(This article belongs to the Section Agricultural Technology)

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19 pages, 15163 KB

Open AccessEditor’s ChoiceArticle

Identification of Geographical Origin of Chinese Chestnuts Using Hyperspectral Imaging with 1D-CNN Algorithm

by Xingpeng Li, Hongzhe Jiang, Xuesong Jiang and Minghong Shi

Agriculture 2021, 11(12), 1274; https://doi.org/10.3390/agriculture11121274 - 15 Dec 2021

Cited by 46 | Viewed by 5065

Abstract

The adulteration in Chinese chestnuts affects the quality, taste, and brand value. The objective of this study was to explore the feasibility of the hyperspectral imaging (HSI) technique to determine the geographical origin of Chinese chestnuts. An HSI system in spectral range of [...] Read more.

The adulteration in Chinese chestnuts affects the quality, taste, and brand value. The objective of this study was to explore the feasibility of the hyperspectral imaging (HSI) technique to determine the geographical origin of Chinese chestnuts. An HSI system in spectral range of 400–1000 nm was applied to identify a total of 417 Chinese chestnuts from three different geographical origins. Principal component analysis (PCA) was preliminarily used to investigate the differences of average spectra of the samples from different geographical origins. A deep-learning-based model (1D-CNN, one-dimensional convolutional neural network) was developed first, and then the model based on full spectra and optimal wavelengths were established for various machine learning methods, including partial least squares-discriminant analysis (PLS-DA) and particle swarm optimization-support vector machine (PSO-SVM). The optimal results based on full spectra for 1D-CNN, PLS-DA, and PSO-SVM models were 97.12%, 97.12%, and 95.68%, respectively. Competitive adaptive reweighted sampling (CARS) and a successive projections algorithm (SPA) were individually utilized for wavelengths selection, and the results of simplified models generally improved. The contrasting results demonstrated that the prediction accuracies of SPA-PLS-DA and 1D-CNN both reached 97.12%, but 1D-CNN presented a higher Kappa coefficient value than SPA-PLS-DA. Meanwhile, the sensitivities and specificities of SPA-PLS-DA and 1D-CNN models were both above 90% for the samples from each geographical origin. These results indicated that both SPA-PLS-DA and 1D-CNN models combined with HSI have great potential for the geographical origin identification of Chinese chestnuts. Full article

(This article belongs to the Special Issue Sensors Applied to Agricultural Products)

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25 pages, 920 KB

Open AccessEditor’s ChoiceReview

Biogenic Selenium Nanoparticles in Animal Nutrition: A Review

by Svetlana Malyugina, Sylvie Skalickova, Jiri Skladanka, Petr Slama and Pavel Horky

Agriculture 2021, 11(12), 1244; https://doi.org/10.3390/agriculture11121244 - 9 Dec 2021

Cited by 51 | Viewed by 8487

Abstract

Selenium still represents a matter of debate in the scientific community. Bionanotechnology has introduced a whole new perspective on selenium use in animal nutrition. In recent years, attention has been focused on selenium nanoparticles prepared by chemical synthesis. Societal pressure directs research in [...] Read more.

Selenium still represents a matter of debate in the scientific community. Bionanotechnology has introduced a whole new perspective on selenium use in animal nutrition. In recent years, attention has been focused on selenium nanoparticles prepared by chemical synthesis. Societal pressure directs research in a “greenway” that is more eco-friendly. Biogenic selenium nanoparticles thus represent a new space for research in the use of this new form of selenium in animal nutrition. Recent research shows that biogenic selenium nanoparticles have low toxicity, improve antioxidant status, and increase the body’s immune response. However, their benefits may be much greater, as numerous in vitro studies have shown. In addition, biogenic selenium nanoparticles possess antimicrobial, antifungal, and anticancer activities. Further research should answer questions on the use of biogenic selenium nanoparticles as a feed supplement in individual categories of livestock, and their safety in terms of long-term supplementation. Full article

(This article belongs to the Special Issue Safety and Efficacy of Feed Additives in Animal Production)

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11 pages, 397 KB

Open AccessEditor’s ChoiceArticle

The Effect of Zinc Oxide Nanoparticles for Enhancing Rice (Oryza sativa L.) Yield and Quality

by Haipeng Zhang, Rui Wang, Zhiqing Chen, Peiyuan Cui, Hao Lu, Yanju Yang and Hongcheng Zhang

Agriculture 2021, 11(12), 1247; https://doi.org/10.3390/agriculture11121247 - 9 Dec 2021

Cited by 76 | Viewed by 8261

Abstract

Zinc oxide nanoparticles (ZnO NPs) have been applied widely in agriculture, and many studies were conducted to evaluate the effect of ZnO NPs on plant growth. So far, few studies have been investigated with regard to the potential effect of ZnO NPs on [...] Read more.

Zinc oxide nanoparticles (ZnO NPs) have been applied widely in agriculture, and many studies were conducted to evaluate the effect of ZnO NPs on plant growth. So far, few studies have been investigated with regard to the potential effect of ZnO NPs on cereal yield formation or Zn content in grains. Herein, we used a pot experiment, which was conducted involving five dosages of Zn (0.3, 0.6, 1.2, 2.4, and 4.8 g Zn pot⁻¹), to evaluate the impacts which ZnO NPs made in rice yield, dry matter accumulation, rice quality and grain Zn contents. The results demonstrated that ZnO NPs increased the production of grain, dry matter accumulation and particulate Zn content. Compared with control treatment, ZnO NPs application presented higher rice yield with more panicle number (4.83–13.14%), spikelets per panicle (4.81–10.69%), 1000-grain weight (3.82–6.62%) and filled grain rate (0.28–2.36%). Additionally, the dry jointing, heading and mature periods, LAI, SPAD values, and photosynthetic potentials of ZnO NPs were all significantly higher relative to treatment without ZnO NPs. The more photosynthetic substances and higher dry matter accumulated in the whole rice growing stage resulted in higher rice grain yield. Furthermore, ZnO NPs increased brown rice rate, milled rice rate, head rice rate, chalkiness size, chalkiness grain rate, chalkiness degree, amylose content and protein content, improving rice processing and appearance qualities. For the Zn nutrition in rice grain, ZnO NPs application significantly increased the Zn content of edible polished rice and promoted the relocation of Zn from the aleurone layer. This study effectively demonstrated that ZnO NPs could be a potential high-performed fertiliser for enhancing rice yield and quality. Full article

(This article belongs to the Topic Soil Fertility and Plant Nutrition for Sustainable Agriculture)

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17 pages, 6820 KB

Open AccessEditor’s ChoiceArticle

Design and Experiment of a Broken Corn Kernel Detection Device Based on the Yolov4-Tiny Algorithm

by Xiaoyu Li, Yuefeng Du, Lin Yao, Jun Wu and Lei Liu

Agriculture 2021, 11(12), 1238; https://doi.org/10.3390/agriculture11121238 - 8 Dec 2021

Cited by 32 | Viewed by 4440

Abstract

At present, the wide application of the CNN (convolutional neural network) algorithm has greatly improved the intelligence level of agricultural machinery. Accurate and real-time detection for outdoor conditions is necessary for realizing intelligence and automation of corn harvesting. In view of the problems [...] Read more.

At present, the wide application of the CNN (convolutional neural network) algorithm has greatly improved the intelligence level of agricultural machinery. Accurate and real-time detection for outdoor conditions is necessary for realizing intelligence and automation of corn harvesting. In view of the problems with existing detection methods for judging the integrity of corn kernels, such as low accuracy, poor reliability, and difficulty in adapting to the complicated and changeable harvesting environment, this paper investigates a broken corn kernel detection device for combine harvesters by using the yolov4-tiny model. Hardware construction is first designed to acquire continuous images and processing of corn kernels without overlap. Based on the images collected, the yolov4-tiny model is then utilized for training recognition of the intact and broken corn kernels samples. Next, a broken corn kernel detection algorithm is developed. Finally, the experiments are carried out to verify the effectiveness of the broken corn kernel detection device. The laboratory results show that the accuracy of the yolov4-tiny model is 93.5% for intact kernels and 93.0% for broken kernels, and the value of precision, recall, and F1 score are 92.8%, 93.5%, and 93.11%, respectively. The field experiment results show that the broken kernel rate obtained by the designed detection device are in good agreement with that obtained by the manually calculated statistic, with differentials at only 0.8%. This study provides a technical reference of a real-time method for detecting a broken corn kernel rate. Full article

(This article belongs to the Special Issue Sensors Applied to Agricultural Products)

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17 pages, 35741 KB

Open AccessEditor’s ChoiceArticle

Design and Simulation of a Garlic Seed Metering Mechanism

by Huiping Guo, Yazhou Cao, Wenyuan Song, Jiao Zhang, Changlin Wang, Chensi Wang, Fuzeng Yang and Lin Zhu

Agriculture 2021, 11(12), 1239; https://doi.org/10.3390/agriculture11121239 - 8 Dec 2021

Cited by 27 | Viewed by 5542

Abstract

According to the agronomic requirements of garlic sowing, the garlic morphology is studied and a garlic seed metering mechanism with excellent seeding performance is designed. Based on this design, a new garlic seeding machine with an adjustable-size seeding device is developed to realize [...] Read more.

According to the agronomic requirements of garlic sowing, the garlic morphology is studied and a garlic seed metering mechanism with excellent seeding performance is designed. Based on this design, a new garlic seeding machine with an adjustable-size seeding device is developed to realize efficient single-seed metering and seeding of different varieties of garlic. Further, the design scheme of the garlic seeder prototype is established, with the key components of the garlic seeding being designed on the basis of the garlic seeding mechanism. To achieve garlic single-seed metering for different varieties of garlic, the optimal adjustment size of the garlic seed metering device is determined through discrete element simulation analysis. A field experiment confirms the effectiveness of applying the proposed garlic planter to field sowing in terms of the metrics of missing seed and multiple seed rates. The results of the discrete element simulation test reveal that an adjustment size of 40 mm yields the best single-seed metering performance. At an operating speed of 15–35 rpm, the metering device can achieve more than an 80% qualification rate of single-seed metering, with a unit speed of 0.628–1.465 m/s. Thus, the developed garlic seeding device meets the requirements of precision sowing in China and can effectively realize the mechanized planting of garlic. Full article

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13 pages, 367 KB

Open AccessEditor’s ChoiceArticle

Technical Efficiency and Technological Gaps of Rice Production in Anambra State, Nigeria

by Chukwujekwu A. Obianefo, John N. Ng’ombe, Agness Mzyece, Blessing Masasi, Ngozi J. Obiekwe and Oluchi O. Anumudu

Agriculture 2021, 11(12), 1240; https://doi.org/10.3390/agriculture11121240 - 8 Dec 2021

Cited by 25 | Viewed by 6973

Abstract

The traditional approach to modeling productive efficiency assumes that technology is constant across the sample. However, farms in different regions may face different production opportunities, and the technologies they employ may differ due to environmental factors. Therefore, rather than using a traditional stochastic [...] Read more.

The traditional approach to modeling productive efficiency assumes that technology is constant across the sample. However, farms in different regions may face different production opportunities, and the technologies they employ may differ due to environmental factors. Therefore, rather than using a traditional stochastic frontier model in such cases, a stochastic meta-frontier (SMF) analysis is recommended to account for environmental factors between regions. It follows that differences in environmental factors between the upland and lowland regions in Anambra State, Nigeria, may result in farmers producing rice under different production and environmental conditions. Using the SMF model, this study, for the first time, determines technical efficiency (TE) and technological gap ratios (TGRs) of rice production from the upland and lowland regions in the Awka North Local Government Area of Anambra State, Nigeria. Our data are from a cross-section sample of randomly selected rice farmers. Results reveal that lowland regional rice producers are on average, significantly more technically efficient (91.7%) than their upland counterparts (84.2%). Additionally, mean TGRs associated with lowland rice farmers are higher (92.1%) than their corresponding upland producers (84.7%). While the upland rice producers are less technically efficient and further away from their full potential, results indicate that both sets of farmers do not use advanced technologies to match the industry’s potential. We suggest that agricultural policy should focus on providing regionally specific technologies, such as improved rice varieties that fit the working environment of the lagging area, to help rice farmers improve their resource efficiency and minimize technological gaps. Full article

(This article belongs to the Special Issue Optimization of Resource Use for Productivity, Efficiency, and Sustainability in Agriculture)

18 pages, 2468 KB

Open AccessEditor’s ChoiceArticle

Effects of Long-Term Straw Management and Potassium Fertilization on Crop Yield, Soil Properties, and Microbial Community in a Rice–Oilseed Rape Rotation

by Jifu Li, Guoyu Gan, Xi Chen and Jialong Zou

Agriculture 2021, 11(12), 1233; https://doi.org/10.3390/agriculture11121233 - 7 Dec 2021

Cited by 35 | Viewed by 5235

Abstract

The present study aims to assess the influences of long-term crop straw returning and recommended potassium fertilization on the dynamic change in rice and oilseed rape yield, soil properties, bacterial and fungal alpha diversity, and community composition in a rice–oilseed rape system. A [...] Read more.

The present study aims to assess the influences of long-term crop straw returning and recommended potassium fertilization on the dynamic change in rice and oilseed rape yield, soil properties, bacterial and fungal alpha diversity, and community composition in a rice–oilseed rape system. A long-term (2011–2020) field experiment was carried out in a selected paddy soil farmland in Jianghan Plain, central China. There were four treatments with three replications: NP, NPK, NPS, and NPKS, where nitrogen (N), phosphate (P), potassium (K), and (S) denote N fertilizer, P fertilizer, K fertilizer, and crop straw, respectively. Results showed that long-term K fertilization and crop straw returning could increase the crop yield at varying degrees for ten years. Compared with the NP treatment, the long-term crop straw incorporation with K fertilizer (NPKS treatment) was found to have the best effect, and the yield rates increased by 23.0% and 20.5% for rice and oilseed rape, respectively. The application of NPK fertilizer for ten years decreased the bacterial and fungal alpha diversity and the relative abundance of dominant bacterial and fungal taxa, whereas continuous straw incorporation had a contradictory effect. NPKS treatment significantly increased the relative abundance of some copiotrophic bacteria (Firmicutes, Gemmatimonadetes, and Proteobacteria) and fungi (Ascomycota). Available K, soil organic matter, dissolved organic carbon, and easily oxidized organic carbon were closely related to alterations in the composition of the dominant bacterial community; easily oxidized organic carbon, dissolved organic carbon, and slowly available K were significantly correlated with the fungal community. We conclude that long-term crop straw returning to the field accompanied with K fertilizer should be employed in rice-growing regions to achieve not only higher crop yield but also the increase in soil active organic carbon and available K content and the improvement of the biological quality of farmland. Full article

(This article belongs to the Special Issue Innovative Conservation Cropping Systems and Practices)

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17 pages, 2877 KB

Open AccessEditor’s ChoiceArticle

Continuous Cropping Changes the Composition and Diversity of Bacterial Communities: A Meta-Analysis in Nine Different Fields with Different Plant Cultivation

by Mohammad Murtaza Alami, Qiuling Pang, Zedan Gong, Tewu Yang, Daiqun Tu, Ouyang Zhen, Weilong Yu, Mohammad Jawad Alami and Xuekui Wang

Agriculture 2021, 11(12), 1224; https://doi.org/10.3390/agriculture11121224 - 3 Dec 2021

Cited by 30 | Viewed by 4425

Abstract

Chinese goldthread (Coptis chinensis Franch.) represents one of the most important medicinal plants with diverse medicinal applications, but it easily suffers from continuous cropping obstacles in the plantation. In this study, we have selected eight different continuously cropped fields with C. chinensis [...] Read more.

Chinese goldthread (Coptis chinensis Franch.) represents one of the most important medicinal plants with diverse medicinal applications, but it easily suffers from continuous cropping obstacles in the plantation. In this study, we have selected eight different continuously cropped fields with C. chinensis and fallow field, providing detailed information regarding the diversity and composition of the rhizospheric bacterial communities. We have found a significant difference between fallow field (LH) and other continuously cropped fields in soil pH; the total content of nitrogen, phosphorus, and potassium; and soil enzyme activities. The results indicate that continuous cropping had a significant effect on soil physicochemical properties and enzyme activities under different plant cultivations. The relative abundance of bacterial phyla was significantly altered among the fields; for example, proteobacteria and Actinobacteria were observed to be higher in continuous cropping of maize (HY6) and lower in sweet potato continuous cropping (HH). Alpha diversity analysis showed that different plants with different years of continuous cropping could change the diversity of bacterial communities, among which the effect of maize and Polygonum multiflorum continuous cropping were most significant. Principle coordinate analysis (PCoA) showed that continuously cropped C. chinensis (LZ) and cabbage continuously cropped for 2 years (HS) were slightly clustered together and separated from LH and others. The results showed that the similarity of the bacterial community in the same crop rotation was higher, which further indicated that the bacterial community structure was significantly altered by the continuous cropping system and plant species. Our study provides a foundation for future agricultural research to improve microbial activity and increase crops/cash-crops productivity under a continuous cropping system and mitigate continuous cropping obstacles. Full article

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12 pages, 271 KB

Open AccessEditor’s ChoiceArticle

Discriminative Power of Geometric Parameters of Different Cultivars of Sour Cherry Pits Determined Using Machine Learning

by Ewa Ropelewska, Kadir Sabanci and Muhammet Fatih Aslan

Agriculture 2021, 11(12), 1212; https://doi.org/10.3390/agriculture11121212 - 2 Dec 2021

Cited by 12 | Viewed by 3131

Abstract

The aim of this study was to develop models based on linear dimensions or shape factors, and the sets of combined linear dimensions and shape factors for discrimination of sour cherry pits of different cultivars (‘Debreceni botermo’, ‘Łutówka’, ‘Nefris’, ‘Kelleris’). The geometric parameters [...] Read more.

The aim of this study was to develop models based on linear dimensions or shape factors, and the sets of combined linear dimensions and shape factors for discrimination of sour cherry pits of different cultivars (‘Debreceni botermo’, ‘Łutówka’, ‘Nefris’, ‘Kelleris’). The geometric parameters were calculated using image processing. The pits of different sour cherry cultivars statistically significantly differed in terms of selected dimensions and shape factors. The discriminative models built based on linear dimensions produced average accuracies of up to 95% for distinguishing the pit cultivars in the case of ‘Nefris’ vs. ‘Kelleris’ and 72% for all four cultivars. The average accuracies for the discriminative models built based on shape factors were up to 95% for the ‘Nefris’ and ‘Kelleris’ pits and 73% for four cultivars. The models combining the linear dimensions and shape factors produced accuracies reaching 96% for the ‘Nefris’ vs. ‘Kelleris’ pits and 75% for all cultivars. The geometric parameters with high discriminative power may be used for distinguishing different cultivars of sour cherry pits. It can be of great importance for practical applications. It may allow avoiding the adulteration and mixing of different cultivars. Full article

15 pages, 1100 KB

Open AccessEditor’s ChoiceArticle

Effects of Chemical Fertilizer Combined with Organic Fertilizer Application on Soil Properties, Citrus Growth Physiology, and Yield

by Lian-Jie Wan, Yang Tian, Man He, Yong-Qiang Zheng, Qiang Lyu, Rang-Jin Xie, Yan-Yan Ma, Lie Deng and Shi-Lai Yi

Agriculture 2021, 11(12), 1207; https://doi.org/10.3390/agriculture11121207 - 30 Nov 2021

Cited by 80 | Viewed by 17040

Abstract

Chemical fertilizer has been excessively used for high yield of citrus around the world, especially in China; meanwhile, it deteriorates the citrus orchard soil environment. To resolve the conflict, the use of organic fertilizer provides a promising solution. However, the data about organic [...] Read more.

Chemical fertilizer has been excessively used for high yield of citrus around the world, especially in China; meanwhile, it deteriorates the citrus orchard soil environment. To resolve the conflict, the use of organic fertilizer provides a promising solution. However, the data about organic fertilizer used in citrus orchard is rarely available. Here, four treatments including CK (no fertilizer), CF (chemical fertilizer), OF + CF (chemical fertilizer reduction combined with organic fertilizer; application of N, P₂O₅, K₂O fertilizer and organic fertilizer is 0.564, 0.236, 0.336 and 10 kg/plant), and BF + CF (chemical fertilizer reduction combined with bioorganic fertilizer; application of N, P₂O₅, K₂O fertilizer and bioorganic fertilizer is 0.508, 0.320, 0.310 and 10 kg/plant) were performed in a ‘Ponkan’ (Citrus reticulata Blanco) orchard to evaluate the effect of organic fertilizer on citrus yield, growth, soil properties etc. when nutrients of fertilizer of each treatment were equal except CK. The data obtained in 2019 and 2020 showed that both OF + CF and BF + CF were beneficial to improve soil fertility (soil physicochemical and microbe properties) and citrus growth physiology (growth, nutrient and photosynthesis), alleviate NO₃⁻-N leaching, and promote yields. Comprehensive evaluation indicated that BF + CF was more effective than OF + CF. Together, organic fertilizer has the potential to substitute partial chemical fertilizer with improvement in soil properties, growth physiology, and yield of citrus. Full article

(This article belongs to the Special Issue Fertilizer Use, Soil Health and Agricultural Sustainability)

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17 pages, 2366 KB

Open AccessEditor’s ChoiceArticle

Modeling the Essential Oil and Trans-Anethole Yield of Fennel (Foeniculum vulgare Mill. var. vulgare) by Application Artificial Neural Network and Multiple Linear Regression Methods

by Mohsen Sabzi-Nojadeh, Gniewko Niedbała, Mehdi Younessi-Hamzekhanlu, Saeid Aharizad, Mohammad Esmaeilpour, Moslem Abdipour, Sebastian Kujawa and Mohsen Niazian

Agriculture 2021, 11(12), 1191; https://doi.org/10.3390/agriculture11121191 - 26 Nov 2021

Cited by 18 | Viewed by 4057

Abstract

Foeniculum vulgare Mill. (commonly known as fennel) is used in the pharmaceutical, cosmetic, and food industries. Fennel widely used as a digestive, carminative, galactagogue and diuretic and in treating gastrointestinal and respiratory disorders. Improving low heritability traits such as essential oil yield (EOY%) [...] Read more.

Foeniculum vulgare Mill. (commonly known as fennel) is used in the pharmaceutical, cosmetic, and food industries. Fennel widely used as a digestive, carminative, galactagogue and diuretic and in treating gastrointestinal and respiratory disorders. Improving low heritability traits such as essential oil yield (EOY%) and trans-anethole yield (TAY%) of fennel by direct selection does not result in rapid gains of EOY% and TAY%. Identification of high-heritable traits and using efficient modeling methods can be a beneficial approach to overcome this limitation and help breeders select the most advantageous traits in medicinal plant breeding programs. The present study aims to compare the performance of the artificial neural network (ANN) and multilinear regression (MLR) to predict the EOY% and TAY% of fennel populations. Stepwise regression (SWR) was used to assess the effect of various input variables. Based on SWR, nine traits—number of days to 50% flowering (NDF50%), number of days to maturity (NDM), final plant height (FPH), number of internodes (NI), number of umbels (NU), seed yield per square meter (SY/m²), number of seeds per plant (NS/P), number of seeds per umbel (NS/U) and 1000-seed weight (TSW)—were chosen as input variables. The network with Sigmoid Axon transfer function and two hidden layers was selected as the final ANN model for the prediction of EOY%, and the TanhAxon function with one hidden layer was used for the prediction of TAY%. The results revealed that the ANN method could predict the EOY% and TAY% with more accuracy and efficiency (R² of EOY% = 0.929, R² of TAY% = 0.777, RMSE of EOY% = 0.544, RMSE of TAY% = 0.264, MAE of EOY% = 0.385 and MAE of TAY% = 0.352) compared with the MLR model (R² of EOY% = 0.553, R² of TAY% = 0.467, RMSE of EOY% = 0.819, RMSE of TAY% = 0.448, MAE of EOY% = 0.624 and MAE of TAY% = 0.452). Based on the sensitivity analysis, SY/m², NDF50% and NS/P were the most important traits to predict EOY% as well as SY/m², NS/U and NDM to predict of TAY%. The results demonstrate the potential of ANNs as a promising tool to predict the EOY% and TAY% of fennel, and they can be used in future fennel breeding programs. Full article

(This article belongs to the Special Issue Digital Innovations in Agriculture)

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16 pages, 4479 KB

Open AccessEditor’s ChoiceArticle

Adapting Root Distribution and Improving Water Use Efficiency via Drip Irrigation in a Jujube (Zizyphus jujube Mill.) Orchard after Long-Term Flood Irrigation

by Zhaoyang Li, Rui Zong, Tianyu Wang, Zhenhua Wang and Jinzhu Zhang

Agriculture 2021, 11(12), 1184; https://doi.org/10.3390/agriculture11121184 - 24 Nov 2021

Cited by 27 | Viewed by 9736

Abstract

Jujube tree yields in dryland saline soils are restricted by water shortages and soil salinity. Converting traditional flood irrigation to drip irrigation would solve water deficit and salt stress. The root distribution reacts primarily to the availability of water and nutrients. However, there [...] Read more.

Jujube tree yields in dryland saline soils are restricted by water shortages and soil salinity. Converting traditional flood irrigation to drip irrigation would solve water deficit and salt stress. The root distribution reacts primarily to the availability of water and nutrients. However, there is little information about the response of jujube roots to the change from flood irrigation to drip irrigation. In this context, a two–year experiment was carried out to reveal the effects of the change from long–term flood irrigation to drip irrigation on soil water, root distribution, fruit yield, and water use efficiency (WUE) of jujube trees. In this study, drip irrigation amounts were designed with three levels, i.e., 880 mm (W1), 660 mm (W2), 440 mm (W3), and the flood irrigation of 1100 mm was designed as the control (CK). The results showed that replacing flood irrigation with drip irrigation significantly altered soil water distribution and increased soil moisture in the topsoil (0–40 cm). In the drip irrigation treatments with high levels, soil water storage in the 0–60 cm soil layer at the flowering and fruit setting, and fruit swelling stages of jujube trees increased significantly compared with the flood irrigation. After two consecutive years of drip irrigation, the treatments with higher irrigation levels increased root length density (RLD) in 0–60 cm soil depth but decreased that in the 60–100 cm depth. In the horizontal direction, higher irrigation levels increased RLD in the distance of 0–50 cm, while reducing RLD in the distance of 50–100 cm. However, the opposite conclusion was obtained in W3 treatment. Additionally, in the second year of drip irrigation, W2 treatment (660 mm) significantly improved yield and WUE, with an increasing of 7.6% for yield and 60.3% for WUE compared to the flood irrigation. In summary, converting flood irrigation to drip irrigation is useful in regulating root distribution and improving WUE, which would be a promising method in jujube cultivation in arid regions. Full article

(This article belongs to the Special Issue Management and Strategies for Improving the Use of Saline Water in Agriculture)

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19 pages, 7964 KB

Open AccessEditor’s ChoiceArticle

Food and Consumer Attitude(s): An Overview of the Most Relevant Documents

by Vítor João Pereira Domingues Martinho

Agriculture 2021, 11(12), 1183; https://doi.org/10.3390/agriculture11121183 - 23 Nov 2021

Cited by 21 | Viewed by 5850

Abstract

Food markets have, at least, two dimensions. One is related to supply, where food marketing, for example, plays a determinant role, namely to promote healthy and balanced consumption. The other dimension is associated with demand, where it is important to understand and bring [...] Read more.

Food markets have, at least, two dimensions. One is related to supply, where food marketing, for example, plays a determinant role, namely to promote healthy and balanced consumption. The other dimension is associated with demand, where it is important to understand and bring insights about consumer attitudes, because they have implications on patterns of food consumption. In this framework, the main objective of this research is to suggest an alternative approach for conducting systematic reviews based on bibliometric analysis and implement it on topics about food and consumer attitudes. For this purpose, the most important bibliographic items (authors, sources, organizations, countries and documents) were identified and later the most relevant documents were reviewed. In addition, 908 documents were selected on 11 December 2020 from the Web of Science Core Collection, for the topics “food” and “consumer attitude*”, and analyzed through bibliometric analysis with the support of the VOSviewer and Gephi software. These documents were also benchmarked with those available in the Scopus scientific database. The approach presented here made it possible to highlight the main insights from the scientific literature related to consumer attitudes to food and bring about further contributions to a literature review supported by bibliometric analysis. This proposal may be known as MB2MBA2 (Methodology Based on Benchmarking of Metadata, from scientific databases, and Bibliometric Assessment and Analysis). This systematic review highlights that organic foods, food neophobia, climate change, marketing strategies and interrelationships between motivations–consumer attitudes–perceptions–purchase intentions–purchase decisions (MAPID) deserved special attention. In addition, MAPID interactions are impacted, among other dimensions, by labelling, branding and trust in the information provided. Future research should further address impacts on consumer attitudes towards food, such as those related to climate-smart agriculture, food 4.0, food security and protection, and climate change and malnutrition. Full article

(This article belongs to the Special Issue Agricultural Food Marketing, Economics and Policies)

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22 pages, 2944 KB

Open AccessEditor’s ChoiceArticle

Crop Models: Important Tools in Decision Support System to Manage Wheat Production under Vulnerable Environments

by Aftab Wajid, Khalid Hussain, Ayesha Ilyas, Muhammad Habib-ur-Rahman, Qamar Shakil and Gerrit Hoogenboom

Agriculture 2021, 11(11), 1166; https://doi.org/10.3390/agriculture11111166 - 19 Nov 2021

Cited by 28 | Viewed by 5296

Abstract

Decision support systems are key for yield improvement in modern agriculture. Crop models are decision support tools for crop management to increase crop yield and reduce production risks. Decision Support System for Agrotechnology Transfer (DSSAT) and an Agricultural System simulator (APSIM), intercomparisons were [...] Read more.

Decision support systems are key for yield improvement in modern agriculture. Crop models are decision support tools for crop management to increase crop yield and reduce production risks. Decision Support System for Agrotechnology Transfer (DSSAT) and an Agricultural System simulator (APSIM), intercomparisons were done to evaluate their performance for wheat simulation. Two-year field experimental data were used for model parameterization. The first year was used for calibration and the second-year data were used for model evaluation and intercomparison. Calibrated models were then evaluated with 155 farmers’ fields surveyed for data in rice-wheat cropping systems. Both models simulated crop phenology, leaf area index (LAI), total dry matter and yield with high goodness of fit to the measured data during both years of evaluation. DSSAT better predicted yield compared to APSIM with a goodness of fit of 64% and 37% during evaluation of 155 farmers’ data. Comparison of individual farmer’s yields showed that the model simulated wheat yield with percent differences (PDs) of −25% to 17% and −26% to 40%, Root Mean Square Errors (RMSEs) of 436 and 592 kg ha⁻¹ with reasonable d-statistics of 0.87 and 0.72 for DSSAT and APSIM, respectively. Both models were used successfully as decision support system tools for crop improvement under vulnerable environments. Full article

(This article belongs to the Special Issue Application of Decision Support Systems in Agriculture)

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27 pages, 477 KB

Open AccessEditor’s ChoiceArticle

Research on Government Subsidy Strategies for the Development of Agricultural Products E-Commerce

by Yaoguang Zhong, Ivan Ka Wai Lai, Fangfang Guo and Huajun Tang

Agriculture 2021, 11(11), 1152; https://doi.org/10.3390/agriculture11111152 - 17 Nov 2021

Cited by 33 | Viewed by 7605

Abstract

In many countries, the governments support the development of local agriculture through subsidization. Subsidizing the sales of agricultural products through E-commerce channels is a way to support the development of agriculture in China. This study aims to develop a profit model and apply [...] Read more.

In many countries, the governments support the development of local agriculture through subsidization. Subsidizing the sales of agricultural products through E-commerce channels is a way to support the development of agriculture in China. This study aims to develop a profit model and apply Stackelberg game theory to determine which type of subsidies and decision-making can provide the maximum benefits for agricultural products E-commerce supply chains. The results indicate that for both centralized decisions and decentralized decisions, the subsidizing to the agricultural cooperative is better than the subsidizing to consumers and no subsidization. The sales volume, preservation level, sales efforts, and overall profit of the agricultural products E-commerce supply chain are significantly higher. It suggests that the government should play a leading role to support the development of agricultural products E-commerce. This study contributes to agricultural research by developing a profit model to examine the effects of different government subsidy strategies on each member of the agricultural online shopping supply chain. Recommendations are provided for agricultural cooperatives, E-commerce platforms, and the government to improve the quality and sales of agricultural products through online shopping channels. Full article

(This article belongs to the Special Issue Agricultural Food Marketing, Economics and Policies)

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14 pages, 2474 KB

Open AccessEditor’s ChoiceArticle

Biodiversity of Culturable Endophytic Actinobacteria Isolated from High Yield Camellia oleifera and Their Plant Growth Promotion Potential

by Ting Xu, Kunpeng Cui, Jiawei Chen, Rui Wang, Xiangnan Wang, Longsheng Chen, Zhen Zhang, Zhilong He, Caixia Liu, Wei Tang, Yonghua Zhu and Yongzhong Chen

Agriculture 2021, 11(11), 1150; https://doi.org/10.3390/agriculture11111150 - 16 Nov 2021

Cited by 16 | Viewed by 4009

Abstract

Camellia oleifera Abel. is one of the world’s four famous woody oil trees and has drawn increasing attention because of its high commercial value. Endophytes are microorganisms inhabiting inside plant tissues, and their functions vary with the change of host status and environmental [...] Read more.

Camellia oleifera Abel. is one of the world’s four famous woody oil trees and has drawn increasing attention because of its high commercial value. Endophytes are microorganisms inhabiting inside plant tissues, and their functions vary with the change of host status and environmental parameters. To deepen our understanding of the interactions between C. oleifera and their endophytic actinobacteria, the present study investigated the four endophytic actinobacterial composition-residing high-yield C. oleifera trees. A total of 156 endophytic actinobacterial isolates were obtained distributed in 17 genera. Among them, Streptomyces was the dominant endophytic actinobacteria, followed by Nocardia, Amycolatopsis, Microbiospora, Micromonospora and other rare actinobacteria genera. Soil characteristics including soil pH and organic matter were found to play crucial roles in shaping the endophytic actinobacterial community composition. Furthermore, all isolates were studied to determine their plant growth-promotion traits, 86.54% could produce Indole 3-Acetic Acid, 16.03% showed nitrogen-fixing, 21.15% showed phosphorus solubilizing, and 35.26% produced siderophore. Under the glasshouse condition, some isolates exhibited growth promotion effects on C. oleifera seedlings with significant increase in spring shoot length and ground diameter. Altogether, this study demonstrated that C. oleifera plants harbored a high diversity and novelty of culturable endophytic actinobacteria, which represent important potential as natural biofertilizers for the high production of C. oleifera. Full article

(This article belongs to the Special Issue Interactions between Microorganisms in Plant Diseases)

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12 pages, 425 KB

Open AccessEditor’s ChoiceArticle

Ammonium Fertilizer Reduces Nitrous Oxide Emission Compared to Nitrate Fertilizer While Yielding Equally in a Temperate Grassland

by Niharika Rahman and Patrick J. Forrestal

Agriculture 2021, 11(11), 1141; https://doi.org/10.3390/agriculture11111141 - 14 Nov 2021

Cited by 28 | Viewed by 6684

Abstract

Emissions of nitrous oxide (N₂O), a potent greenhouse gas, are a challenge associated with application of nitrogen (N) fertilizers to soil. However, N source selection can play a role in reducing these emissions. Nitrous oxide emissions were measured from ammonium (ammonium [...] Read more.

Emissions of nitrous oxide (N₂O), a potent greenhouse gas, are a challenge associated with application of nitrogen (N) fertilizers to soil. However, N source selection can play a role in reducing these emissions. Nitrous oxide emissions were measured from ammonium (ammonium sulfate) and nitrate (calcium nitrate) fertilizers over one year in temperate grassland using the closed static chamber method. Nitrogen was applied at a system representative rate of 220 kg ha⁻¹ y⁻¹ in six split applications. Cumulative annual N₂O-N emissions were 0.29 kg ha⁻¹ for the control, 1.07 kg ha⁻¹ for the ammonium fertilizer and significantly higher at 2.54 kg ha⁻¹ for the nitrate fertilizer. The annual emission factor (EF) for the ammonium fertilizer was 0.35% vs 1.02% for the nitrate fertilizer, a 66% reduction in the EF for ammonium vs nitrate and a 2.9 times higher EF for nitrate compared with ammonium. No difference in grass yield or N uptake was detected between fertilizers. This study shows that an ammonium fertilizer produces the same yield and N efficiency as a nitrate fertilizer with lower N₂O emissions. The results also demonstrate that the nitrate portion of fertilizers is a key factor in N₂O emissions in temperate grassland. This work is the first of its kind detailing the annual EF of both a solely ammonium-N and a solely nitrate-N fertilizer we could find. Full article

(This article belongs to the Special Issue Strategies for Nitrous Oxide Emission Mitigation in Agrosystems)

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15 pages, 1211 KB

Open AccessEditor’s ChoiceArticle

The Impact of LED Light Spectrum on the Growth, Morphological Traits, and Nutritional Status of ‘Elizium’ Romaine Lettuce Grown in an Indoor Controlled Environment

by Bożena Matysiak, Stanisław Kaniszewski, Jacek Dyśko, Waldemar Kowalczyk, Artur Kowalski and Maria Grzegorzewska

Agriculture 2021, 11(11), 1133; https://doi.org/10.3390/agriculture11111133 - 12 Nov 2021

Cited by 25 | Viewed by 9326

Abstract

The study examined the influence of light quality on the growth and nutritional status of romaine lettuce grown in deep water culture with a floating raft system using two different nutrient solutions. Four spectra of LED light were used with different ratios of [...] Read more.

The study examined the influence of light quality on the growth and nutritional status of romaine lettuce grown in deep water culture with a floating raft system using two different nutrient solutions. Four spectra of LED light were used with different ratios of R, G, and B lights (80:10:10, 70:10:20, 60:10:30, and 70:18:12). Two nutrient solutions with a low (A) and moderately high (B) nutrient content were used. Regardless of the nutrient solution, the RGB 70:18:12 light promoted the production of leaf biomass as well as inhibited the accumulation of K and Mg in the leaves. Moreover, those plants were characterized by a low Nitrogen Balance Index (NBI) and a high flavonol index. In the last week of cultivation, there was a strong decrease in K, P, and nitrates in the nutrient solution, and an increase in Ca. In the final stage of growth, symptoms of withering of the tips of young leaves (tipburn) were observed on the plants. The most damage was observed on the plants growing under 70:10:20, 70:18:12, and with the higher concentration of minerals in the solution (B). Full article

(This article belongs to the Special Issue Impact of Light on Horticultural Crops)

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17 pages, 5488 KB

Open AccessEditor’s ChoiceArticle

Estimation of Soil Nutrient Content Using Hyperspectral Data

by Yiping Peng, Lu Wang, Li Zhao, Zhenhua Liu, Chenjie Lin, Yueming Hu and Luo Liu

Agriculture 2021, 11(11), 1129; https://doi.org/10.3390/agriculture11111129 - 11 Nov 2021

Cited by 42 | Viewed by 8050

Abstract

Soil nutrients play a vital role in plant growth and thus the rapid acquisition of soil nutrient content is of great significance for agricultural sustainable development. Hyperspectral remote-sensing techniques allow for the quick monitoring of soil nutrients. However, at present, obtaining accurate estimates [...] Read more.

Soil nutrients play a vital role in plant growth and thus the rapid acquisition of soil nutrient content is of great significance for agricultural sustainable development. Hyperspectral remote-sensing techniques allow for the quick monitoring of soil nutrients. However, at present, obtaining accurate estimates proves to be difficult due to the weak spectral features of soil nutrients and the low accuracy of soil nutrient estimation models. This study proposed a new method to improve soil nutrient estimation. Firstly, for obtaining characteristic variables, we employed partial least squares regression (PLSR) fit degree to select an optimal screening algorithm from three algorithms (Pearson correlation coefficient, PCC; least absolute shrinkage and selection operator, LASSO; and gradient boosting decision tree, GBDT). Secondly, linear (multi-linear regression, MLR; ridge regression, RR) and nonlinear (support vector machine, SVM; and back propagation neural network with genetic algorithm optimization, GABP) algorithms with 10-fold cross-validation were implemented to determine the most accurate model for estimating soil total nitrogen (TN), total phosphorus (TP), and total potassium (TK) contents. Finally, the new method was used to map the soil TK content at a regional scale using the soil component spectral variables retrieved by the fully constrained least squares (FCLS) method based on an image from the HuanJing-1A Hyperspectral Imager (HJ-1A HSI) of the Conghua District of Guangzhou, China. The results identified the GBDT-GABP was observed as the most accurate estimation method of soil TN ( of 0.69, the root mean square error of cross-validation (RMSECV) of 0.35 g kg⁻¹ and ratio of performance to interquartile range (RPIQ) of 2.03) and TP ( of 0.73, RMSECV of 0.30 g kg⁻¹ and RPIQ = 2.10), and the LASSO-GABP proved to be optimal for soil TK estimations ( of 0.82, RMSECV of 3.39 g kg⁻¹ and RPIQ = 3.57). Additionally, the highly accurate LASSO-GABP-estimated soil TK (R² = 0.79) reveals the feasibility of the LASSO-GABP method to retrieve soil TK content at the regional scale. Full article

(This article belongs to the Special Issue Digital Innovations in Agriculture)

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18 pages, 3083 KB

Open AccessEditor’s ChoiceArticle

Predicting Possible Distribution of Tea (Camellia sinensis L.) under Climate Change Scenarios Using MaxEnt Model in China

by Yuncheng Zhao, Mingyue Zhao, Lei Zhang, Chunyi Wang and Yinlong Xu

Agriculture 2021, 11(11), 1122; https://doi.org/10.3390/agriculture11111122 - 10 Nov 2021

Cited by 43 | Viewed by 5997

Abstract

Climate change has dramatic impacts on the growth and the geographical distribution of tea (Camellia sinensis L.). Assessing the potential distribution of tea will help decision makers to formulate appropriate adaptation measures to use the altered climatic resources and avoid the damage [...] Read more.

Climate change has dramatic impacts on the growth and the geographical distribution of tea (Camellia sinensis L.). Assessing the potential distribution of tea will help decision makers to formulate appropriate adaptation measures to use the altered climatic resources and avoid the damage from climate hazards. The objective in this study is to model the current and future distribution of tea species based on the four SSPs scenarios using the MaxEnt model in China. For the modeling procedure, tea growth records in 410 sites and 9 climate variables were used in this paper. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the model. The AUC value was over 0.9 in this study, showing the excellent simulation result of the model. In relation to the current distribution, areas of 82.01 × 10⁴ km² (8.51% of total land area in China), 115.97 × 10⁴ km² (12.03% of total land area in China), and 67.14 × 10⁴ km² (6.97% of total land area in China) were recognized as Marginal, Medium, and Optimal climate suitable habitats for tea over China. Compared to the current distribution, most of the Optimal suitability areas in southeast China would be lost in four scenarios. The area of Marginal and Medium suitable habitats would expand in SSP370 and SSP585, especially in 2041–2061 and 2081–2100. The suitable area of tea would expand northwards and westwards, suggesting that additional new suitable habitats could be created for tea production with the future climate change, especially in Shandong, Henan, Guizhou, and Yunnan Provinces. This research would provide vital scientific understanding for policy making on tea production, tea garden site chosen and adopyion of adaptation methods in the future. Full article

(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)

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11 pages, 978 KB

Open AccessEditor’s ChoiceArticle

Biochar Reduces the Adverse Effect of Saline Water on Soil Properties and Wheat Production Profitability

by Mohamed E. A. El-sayed, Mohamed Hazman, Ayman Gamal Abd El-Rady, Lal Almas, Mike McFarland, Ali Shams El Din and Steve Burian

Agriculture 2021, 11(11), 1112; https://doi.org/10.3390/agriculture11111112 - 9 Nov 2021

Cited by 21 | Viewed by 4087

Abstract

The goal of this study is to assess the use of saline groundwater in combination with soil amendments to increase the efficiency of wheat production in new agricultural soil in Egypt. The experiment was conducted during the two consecutive growing seasons, 2019/2020 and [...] Read more.

The goal of this study is to assess the use of saline groundwater in combination with soil amendments to increase the efficiency of wheat production in new agricultural soil in Egypt. The experiment was conducted during the two consecutive growing seasons, 2019/2020 and 2020/2021, at the Shandaweel Agricultural Research Station, Sohag, Egypt. In this study, plants of Shandaweel 1 spring bread wheat cultivar were grown under the combinations of the two water treatments, i.e., freshwater (307.2 ppm) and saline water (3000 ppm (NaCl + MgCl₂)) representing groundwater in Egypt delivered by drip irrigation and the two biochar rates, i.e., zero and 4.8 ton/ha as a soil amendment. The cob corn biochar (CCB) was synthesized by using the slow pyrolysis process (one hour at 350 °C). The results revealed that saline water reduced the grain yield ratio by 8.5%, 11.0%, and 9.7% compared to non-saline water during seasons 2019/2020 and 2020/2021 and over seasons, respectively. Concerning, combined over seasons, the biochar addition enhanced the grain yield by 5.6% and 13.8% compared to non-biochar addition under fresh and saline irrigation water conditions, respectively. Thus, the results indicated and led to a preliminary recommendation that saline groundwater is a viable source of irrigation water and that biochar seemed to alleviate salinity stress on wheat production and in reclaimed soils of Egypt. Full article

(This article belongs to the Topic Soil Fertility and Plant Nutrition for Sustainable Agriculture)

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19 pages, 6252 KB

Open AccessEditor’s ChoiceArticle

Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning

by Yan Hu, Lijia Xu, Peng Huang, Xiong Luo, Peng Wang and Zhiliang Kang

Agriculture 2021, 11(11), 1106; https://doi.org/10.3390/agriculture11111106 - 6 Nov 2021

Cited by 35 | Viewed by 4059

Abstract

A rapid and nondestructive tea classification method is of great significance in today’s research. This study uses fluorescence hyperspectral technology and machine learning to distinguish Oolong tea by analyzing the spectral features of tea in the wavelength ranging from 475 to 1100 nm. [...] Read more.

A rapid and nondestructive tea classification method is of great significance in today’s research. This study uses fluorescence hyperspectral technology and machine learning to distinguish Oolong tea by analyzing the spectral features of tea in the wavelength ranging from 475 to 1100 nm. The spectral data are preprocessed by multivariate scattering correction (MSC) and standard normal variable (SNV), which can effectively reduce the impact of baseline drift and tilt. Then principal component analysis (PCA) and t-distribution random neighborhood embedding (t-SNE) are adopted for feature dimensionality reduction and visual display. Random Forest-Recursive Feature Elimination (RF-RFE) is used for feature selection. Decision Tree (DT), Random Forest Classification (RFC), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are used to establish the classification model. The results show that MSC-RF-RFE-SVM is the best model for the classification of Oolong tea in which the accuracy of the training set and test set is 100% and 98.73%, respectively. It can be concluded that fluorescence hyperspectral technology and machine learning are feasible to classify Oolong tea. Full article

(This article belongs to the Special Issue The Application of Machine Learning in Agriculture)

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17 pages, 634 KB

Open AccessEditor’s ChoiceArticle

Crop Insurance, Land Productivity and the Environment: A Way forward to a Better Understanding

by Agnieszka Kurdyś-Kujawska, Agnieszka Sompolska-Rzechuła, Joanna Pawłowska-Tyszko and Michał Soliwoda

Agriculture 2021, 11(11), 1108; https://doi.org/10.3390/agriculture11111108 - 6 Nov 2021

Cited by 24 | Viewed by 5632

Abstract

Providing farmers with effective risk management tools and increasing the productivity of factors of production, while limiting negative effects on the environment, is an important challenge for the current EU agricultural policy. The aim of this research is to identify and evaluate the [...] Read more.

Providing farmers with effective risk management tools and increasing the productivity of factors of production, while limiting negative effects on the environment, is an important challenge for the current EU agricultural policy. The aim of this research is to identify and evaluate the relationship between crop insurance and land productivity in the context of environmental effects. The study covered farms with crop insurance participating in the Polish FADN system. The article uses the TOPSIS method of organizing objects. We classify farms in terms of land productivity and examine the relationship between these results and the value of insurance coverage. In our conceptual and empirical framework, we recognize that there is a mutual relationship between crop insurance, land productivity and the environment. Our empirical results show that the level of insurance coverage may support the increase in land productivity, indirectly affecting the environment. Farms with the highest productivity level were characterized by an average value of insurance that was double that compared to farms with the lowest productivity level. Full article

(This article belongs to the Special Issue Challenges of Contemporary Agriculture, Rural Areas and Agri-Food Supply Chain under Conditions of Uncertainty)

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24 pages, 6336 KB

Open AccessEditor’s ChoiceArticle

Simplified and Hybrid Remote Sensing-Based Delineation of Management Zones for Nitrogen Variable Rate Application in Wheat

by Mohammad Rokhafrouz, Hooman Latifi, Ali A. Abkar, Tomasz Wojciechowski, Mirosław Czechlowski, Ali Sadeghi Naieni, Yasser Maghsoudi and Gniewko Niedbała

Agriculture 2021, 11(11), 1104; https://doi.org/10.3390/agriculture11111104 - 5 Nov 2021

Cited by 21 | Viewed by 4311

Abstract

Enhancing digital and precision agriculture is currently inevitable to overcome the economic and environmental challenges of the agriculture in the 21st century. The purpose of this study was to generate and compare management zones (MZ) based on the Sentinel-2 satellite data for variable [...] Read more.

Enhancing digital and precision agriculture is currently inevitable to overcome the economic and environmental challenges of the agriculture in the 21st century. The purpose of this study was to generate and compare management zones (MZ) based on the Sentinel-2 satellite data for variable rate application of mineral nitrogen in wheat production, calculated using different remote sensing (RS)-based models under varied soil, yield and crop data availability. Three models were applied, including (1) a modified “RS- and threshold-based clustering”, (2) a “hybrid-based, unsupervised clustering”, in which data from different sources were combined for MZ delineation, and (3) a “RS-based, unsupervised clustering”. Various data processing methods including machine learning were used in the model development. Statistical tests such as the Paired Sample T-test, Kruskal–Wallis H-test and Wilcoxon signed-rank test were applied to evaluate the final delineated MZ maps. Additionally, a procedure for improving models based on information about phenological phases and the occurrence of agricultural drought was implemented. The results showed that information on agronomy and climate enables improving and optimizing MZ delineation. The integration of prior knowledge on new climate conditions (drought) in image selection was tested for effective use of the models. Lack of this information led to the infeasibility of obtaining optimal results. Models that solely rely on remote sensing information are comparatively less expensive than hybrid models. Additionally, remote sensing-based models enable delineating MZ for fertilizer recommendations that are temporally closer to fertilization times. Full article

(This article belongs to the Special Issue Digital Innovations in Agriculture)

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16 pages, 1042 KB

Open AccessEditor’s ChoiceArticle

Yield, Economic Benefit, Soil Water Balance, and Water Use Efficiency of Intercropped Maize/Potato in Responses to Mulching Practices on the Semiarid Loess Plateau

by Junhong Xie, Linlin Wang, Lingling Li, Sumera Anwar, Zhuzhu Luo, Effah Zechariah and Setor Kwami Fudjoe

Agriculture 2021, 11(11), 1100; https://doi.org/10.3390/agriculture11111100 - 4 Nov 2021

Cited by 29 | Viewed by 4517

Abstract

Increasing agricultural productivity without undermining further the integrity of the Earth’s environmental systems such as soil water balance are important tasks to ensure food security for an increasing global population in rainfed agriculture. The impact of intercropping maize (Zea mays L.) with [...] Read more.

Increasing agricultural productivity without undermining further the integrity of the Earth’s environmental systems such as soil water balance are important tasks to ensure food security for an increasing global population in rainfed agriculture. The impact of intercropping maize (Zea mays L.) with potato (Solanum tuberosum L.) on yield, land equivalent ratios (LER), water equivalent ratio (WER), water use, energy output, and net economic return were examined under seven planting systems: potato grown solely or intercropped on the flat field without mulching, maize grown solely or intercropped with potato on ridges or flat field with or without plastic film mulched. The three intercropping systems had 3–13% less water use than the monocropping. Among the intercropped systems, flat field caused more depletion of soil water than ridged field for both years. Compared to monocultures, intercropping with plastic film mulching and ridging significantly increased LER and WER. Meanwhile, intercropping with mulching and ridging significantly increased net economic return and energy output by 8% and 24%, respectively, when compared to monocropping. These results suggest that maize under plastic film mulched ridge-furrow plot intercropped with potato under flat plot without mulching increased energy output, net economic return, and water use efficiency without increasing soil water depletion, which could be an optimal intercropping system for the semiarid farmland on the western Loess Plateau. Full article

(This article belongs to the Special Issue Intercropping Systems for Sustainable Agriculture)

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21 pages, 1247 KB

Open AccessEditor’s ChoiceArticle

Study on Livelihood Vulnerability and Adaptation Strategies of Farmers in Areas Threatened by Different Disaster Types under Climate Change

by Xue Yang, Shili Guo, Xin Deng, Wei Wang and Dingde Xu

Agriculture 2021, 11(11), 1088; https://doi.org/10.3390/agriculture11111088 - 3 Nov 2021

Cited by 38 | Viewed by 8215

Abstract

The intensification of global climate change leads to frequent mountain torrents, landslides, debris flows and other disasters, which seriously threaten the safety of residents’ lives and property. However, few studies have compared and analyzed the livelihood vulnerability and adaptation strategies of farmers in [...] Read more.

The intensification of global climate change leads to frequent mountain torrents, landslides, debris flows and other disasters, which seriously threaten the safety of residents’ lives and property. However, few studies have compared and analyzed the livelihood vulnerability and adaptation strategies of farmers in different disaster-threatened areas under the background of climate change. Based on survey data of 327 households in the areas threatened by mountain floods, landslides and debris flow in Sichuan Province, this study analyzed the characteristics of livelihood vulnerability and adaptation strategies of households in the areas threatened by different disaster types and constructed multinomial logistic regression models to explore their correlations. The findings show that: (1) The livelihood vulnerability indices of farmers in different hazard types showed different characteristics. Among them, the livelihood vulnerability index of farmers in landslide-threatened zones is the highest, followed by the livelihood vulnerability index of farmers in debris-flow-threatened zones, and finally the livelihood vulnerability index of farmers in flash flood threat zones. At the same time, all three natural hazards show a trend of higher vulnerability in the sensitivity dimension than in the exposure and livelihood resilience dimensions. (2) The nonfarming livelihood strategy is the main livelihood strategy for farmers in different disaster-type-threatened areas. At the same time, the vulnerability of farmers choosing the nonfarming livelihood strategy is much higher than that of farmers choosing the part-time livelihood strategy and pure farming livelihood strategy, and the vulnerability of sensitivity dimension is higher than that of the exposure dimension and livelihood resilience dimension. (3) For farmers in landslide- and debris-flow-threatened areas, livelihood resilience is an important factor affecting their livelihood strategy. There was a positive correlation between livelihood resilience and farmers’ choice of pure agricultural livelihood strategies in these two natural-disaster-threatened areas. This study deepens our understanding of the characteristics and relationships of farmers’ livelihood vulnerability and adaptation strategies under different disaster types in the context of climate change, and then provides the reference basis for the formulation of livelihood-adaptive capacity promotion-related policy. Full article

(This article belongs to the Special Issue Developing the Evidence Base for Rural Development Policies and Interventions to Address Novel and Emerging Societal Problems)

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19 pages, 4270 KB

Open AccessEditor’s ChoiceArticle

Prediction of Wheat Stripe Rust Occurrence with Time Series Sentinel-2 Images

by Chao Ruan, Yingying Dong, Wenjiang Huang, Linsheng Huang, Huichun Ye, Huiqin Ma, Anting Guo and Yu Ren

Agriculture 2021, 11(11), 1079; https://doi.org/10.3390/agriculture11111079 - 1 Nov 2021

Cited by 28 | Viewed by 4230

Abstract

Wheat stripe rust has a severe impact on wheat yield and quality. An effective prediction method is necessary for food security. In this study, we extract the optimal vegetation indices (VIs) sensitive to stripe rust at different time-periods, and develop a wheat stripe [...] Read more.

Wheat stripe rust has a severe impact on wheat yield and quality. An effective prediction method is necessary for food security. In this study, we extract the optimal vegetation indices (VIs) sensitive to stripe rust at different time-periods, and develop a wheat stripe rust prediction model with satellite images to realize the multi-temporal prediction. First, VIs related to stripe rust stress are extracted as candidate features for disease prediction from time series Sentinel-2 images. Then, the optimal VI combinations are selected using sequential forward selection (SFS). Finally, the occurrence of wheat stripe rust in different time-periods is predicted using the support vector machine (SVM) method. The results of the features selected demonstrate that, before the jointing period, the optimal VIs are related to the biomass, pigment, and moisture of wheat. After the jointing period, the red-edge VIs related to the crop health status play important roles. The overall accuracy and Kappa coefficient of the prediction model, which is based on SVM, is generally higher than those of the k-nearest neighbor (KNN) and back-propagation neural network (BPNN) methods. The SVM method is more suitable for time series predictions of wheat stripe rust. The model obtained accuracy based on the optimal VI combinations and the SVM increased over time; the highest accuracy was 86.2%. These results indicate that the prediction model can provide guidance and suggestions for early disease prevention of the study site, and the method combines time series Sentinel-2 images and the SVM, which can be used to predict wheat stripe rust. Full article

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14 pages, 3018 KB

Open AccessEditor’s ChoiceArticle

Application of Ultrasound and Curing Agent during Osmotic Dehydration to Improve the Quality Properties of Freeze-Dried Yellow Peach (Amygdalus persica) Slices

by Yuanming Chu, Saichao Wei, Zhaoyang Ding, Jun Mei and Jing Xie

Agriculture 2021, 11(11), 1069; https://doi.org/10.3390/agriculture11111069 - 30 Oct 2021

Cited by 24 | Viewed by 3434

Abstract

This study aimed to improve the quality of freeze-dried yellow peaches (Amygdalus persica). Yellow peaches were pretreated with osmotic dehydration for 15 min prior to vacuum-freeze drying and supplemented with different ultrasonic power levels (180 W, 240 W, 300 W) and [...] Read more.

This study aimed to improve the quality of freeze-dried yellow peaches (Amygdalus persica). Yellow peaches were pretreated with osmotic dehydration for 15 min prior to vacuum-freeze drying and supplemented with different ultrasonic power levels (180 W, 240 W, 300 W) and a curing agent (calcium lactobionate, CaLa) to investigate the effects on the quality of freeze-dried yellow peach slices. After vacuum freeze-drying the yellow peach slices for 48 h, their moisture, color, texture, microstructure, total phenol (TP) content and oligomeric proantho-cyanidin (OPC) content were determined. It was found that the auxiliary ultrasonic power with various levels, especially powered at 240 W, produced very favorable effects on the quality characteristics of freeze-dried yellow peaches. The average pore size of USOD-240 W samples was reduced by 57.07% compared with that of the FD samples. In terms of nutrient maintenance, USOD-240 W can also prevent nutrient loss to the greatest extent. The TP content (5.40 mg/g) and OPC content (14.42 mg/g) were always highest in each pretreatment. The addition of CaLa can further improve the quality of yellow peach slices. Overall, the application of ultrasound and CaLa to improve the quality of freeze-dried yellow peach slices along with osmotic dehydration before freeze-drying is a method worth considering. Full article

(This article belongs to the Section Agricultural Product Quality and Safety)

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13 pages, 20799 KB

Open AccessEditor’s ChoiceArticle

Tomato Young Fruits Detection Method under Near Color Background Based on Improved Faster R-CNN with Attention Mechanism

by Peng Wang, Tong Niu and Dongjian He

Agriculture 2021, 11(11), 1059; https://doi.org/10.3390/agriculture11111059 - 28 Oct 2021

Cited by 40 | Viewed by 4058

Abstract

The information of tomato young fruits acquisition has an important impact on monitoring fruit growth, early control of pests and diseases and yield estimation. It is of great significance for timely removing young fruits with abnormal growth status, improving the fruits quality, and [...] Read more.

The information of tomato young fruits acquisition has an important impact on monitoring fruit growth, early control of pests and diseases and yield estimation. It is of great significance for timely removing young fruits with abnormal growth status, improving the fruits quality, and maintaining high and stable yields. Tomato young fruits are similar in color to the stems and leaves, and there are interference factors, such as fruits overlap, stems and leaves occlusion, and light influence. In order to improve the detection accuracy and efficiency of tomato young fruits, this paper proposes a method for detecting tomato young fruits with near color background based on improved Faster R-CNN with an attention mechanism. First, ResNet50 is used as the feature extraction backbone, and the feature map extracted is optimized through Convolutional Block Attention Module (CBAM). Then, Feature Pyramid Network (FPN) is used to integrate high-level semantic features into low-level detailed features to enhance the model sensitivity of scale. Finally, Soft Non-Maximum Suppression (Soft-NMS) is used to reduce the missed detection rate of overlapping fruits. The results show that the mean Average Precision (mAP) of the proposed method reaches 98.46%, and the average detection time per image is only 0.084 s, which can achieve the real-time and accurate detection of tomato young fruits. The research shows that the method in this paper can efficiently identify tomato young fruits, and provides a better solution for the detection of fruits with near color background. Full article

(This article belongs to the Section Agricultural Technology)

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15 pages, 7031 KB

Open AccessEditor’s ChoiceArticle

Evaluation of Deep Learning for Automatic Multi-View Face Detection in Cattle

by Beibei Xu, Wensheng Wang, Leifeng Guo, Guipeng Chen, Yaowu Wang, Wenju Zhang and Yongfeng Li

Agriculture 2021, 11(11), 1062; https://doi.org/10.3390/agriculture11111062 - 28 Oct 2021

Cited by 51 | Viewed by 6460

Abstract

Individual identification plays an important part in disease prevention and control, traceability of meat products, and improvement of agricultural false insurance claims. Automatic and accurate detection of cattle face is prior to individual identification and facial expression recognition based on image analysis technology. [...] Read more.

Individual identification plays an important part in disease prevention and control, traceability of meat products, and improvement of agricultural false insurance claims. Automatic and accurate detection of cattle face is prior to individual identification and facial expression recognition based on image analysis technology. This paper evaluated the possibility of the cutting-edge object detection algorithm, RetinaNet, performing multi-view cattle face detection in housing farms with fluctuating illumination, overlapping, and occlusion. Seven different pretrained CNN models (ResNet 50, ResNet 101, ResNet 152, VGG 16, VGG 19, Densenet 121 and Densenet 169) were fine-tuned by transfer learning and re-trained on the dataset in the paper. Experimental results showed that RetinaNet incorporating the ResNet 50 was superior in accuracy and speed through performance evaluation, which yielded an average precision score of 99.8% and an average processing time of 0.0438 s per image. Compared with the typical competing algorithms, the proposed method was preferable for cattle face detection, especially in particularly challenging scenarios. This research work demonstrated the potential of artificial intelligence towards the incorporation of computer vision systems for individual identification and other animal welfare improvements. Full article

(This article belongs to the Special Issue Digital Innovations in Agriculture)

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15 pages, 5880 KB

Open AccessEditor’s ChoiceArticle

Use of Recovered Struvite and Ammonium Nitrate in Fertigation in Tomato (Lycopersicum esculentum) Production for boosting Circular and Sustainable Horticulture

by Mar Carreras-Sempere, Rafaela Caceres, Marc Viñas and Carmen Biel

Agriculture 2021, 11(11), 1063; https://doi.org/10.3390/agriculture11111063 - 28 Oct 2021

Cited by 25 | Viewed by 4670

Abstract

Struvite and ammonium nitrate are products obtained from widely studied processes to remove phosphorus (P) and nitrogen (N) from waste streams. To boost circularity in horticulture, these recovered products should be applied to edible crops. Particularly, struvite has not been implemented in fertigation [...] Read more.

Struvite and ammonium nitrate are products obtained from widely studied processes to remove phosphorus (P) and nitrogen (N) from waste streams. To boost circularity in horticulture, these recovered products should be applied to edible crops. Particularly, struvite has not been implemented in fertigation as the unique source of P fertilizer. Therefore, a soilless system greenhouse experiment was conducted for tomato crops during two growing seasons. This study aims to compare the agronomic and environmental effectiveness of recovered products used in a nutrient solution for fertigation (NS) to synthetic fertilizer treatment. Moreover, two different N concentrations of the NS were tested to evaluate the impact on the N-leaching. Additionally, struvite dissolution tests were performed to ensure its solubility. Satisfactory results of struvite solubilization were obtained. Results show that both nutrient-recovered products can be used as fertilizers in NS, due to their non-statistical significance in total yield production and fruit quality. However, ammonium nitrate treatment, depending on the crop variety, showed a lower marketable yield. Moreover, the variation on N concentration input exhibited leachate concentration differences, with N leached percentage values from 36 to 13%. These results give deeper insights into the future potential utilization of nutrient-recovered products and technical data to optimize fertigation strategies. Full article

(This article belongs to the Special Issue System Efficiency and Resource Recovery in Circular Horticulture)

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20 pages, 2438 KB

Open AccessEditor’s ChoiceArticle

Acidification Effects on In Situ Ammonia Emissions and Cereal Yields Depending on Slurry Type and Application Method

by Christian Wagner, Tavs Nyord, Annette Vibeke Vestergaard, Sasha Daniel Hafner and Andreas Siegfried Pacholski

Agriculture 2021, 11(11), 1053; https://doi.org/10.3390/agriculture11111053 - 27 Oct 2021

Cited by 31 | Viewed by 4324

Abstract

Field application of organic slurries contributes considerably to emissions of ammonia (NH₃) which causes sever environmental damage and can result in lower nitrogen (N) fertilizer efficiency. In recent years, field acidification systems have been introduced to reduce such NH₃ emissions. [...] Read more.

Field application of organic slurries contributes considerably to emissions of ammonia (NH₃) which causes sever environmental damage and can result in lower nitrogen (N) fertilizer efficiency. In recent years, field acidification systems have been introduced to reduce such NH₃ emissions. However, combined field data on ammonia emissions and N use efficiency of acidified slurries, in particular by practical acidification systems, are scarce. Here, we present for the first time a simultaneous in situ assessment of the effects of acidification of five different organic slurries with a commercial acidifications system combined with different application techniques. The analysis was performed in randomized plot trials in winter wheat and spring barley after two applications to each crop (before tillering and after flag leave emergence) in year 2014 in Denmark. Slurry types included cattle slurry, mink slurry, pig slurry, anaerobic digestate, and the liquid phase of anaerobic digestate. Tested application techniques were trail hose application with and without slurry acidification in winter wheat and slurry injection and incorporation compared to trail hose application with and without acidification in spring barley. Slurries were applied on 9 m × 9 m plots separated by buffer areas of the same dimension. Ammonia emission was determined by a combination of semi-quantitative acid traps scaled by absolute emissions obtained from Draeger Tube Method dynamic chamber measurements. Experimental results were analysed by mixed effects models and HSD post hoc test (p < 0.05). Significant and almost quantitative NH₃ emission reduction compared to trail hose application was observed in the barley trial by slurry incorporation of acidified slurry (89% reduction) and closed slot injection (96% reduction), while incorporation alone decreased emissions by 60%. In the two applications to winter wheat, compared to trail hose application of non-acidified slurry, acidification reduced NH₃ emissions by 61% and 67% in cattle slurry, in anaerobic digestate by 45% and 57% and liquid phase of anaerobic digestate by 58%, respectively. Similar effects but on a lower emission level were observed in mink slurry, while acidification showed almost no effect in pig slurry. Acidifying animal manure with a commercial system was confirmed to consistently reduce NH₃ emissions of most slurry types, and emission reductions were similar as from experimental acidification systems. However, failure to reduce ammonia emissions in pig slurry hint to technical limitations of such systems. Winter wheat and spring barley yields were only partly significantly increased by use of ammonia emission mitigation measures, while there were significant positive effects on apparent nitrogen use efficiency (+17–28%). The assessment of the agronomic effects of acidification requires further investigations. Full article

(This article belongs to the Special Issue Nitrogen Fertilization in Crop Production)

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21 pages, 5250 KB

Open AccessEditor’s ChoiceArticle

Mechanical Control with a Deep Learning Method for Precise Weeding on a Farm

by Chung-Liang Chang, Bo-Xuan Xie and Sheng-Cheng Chung

Agriculture 2021, 11(11), 1049; https://doi.org/10.3390/agriculture11111049 - 26 Oct 2021

Cited by 32 | Viewed by 10373

Abstract

This paper presents a mechanical control method for precise weeding based on deep learning. Deep convolutional neural network was used to identify and locate weeds. A special modular weeder was designed, which can be installed on the rear of a mobile platform. An [...] Read more.

This paper presents a mechanical control method for precise weeding based on deep learning. Deep convolutional neural network was used to identify and locate weeds. A special modular weeder was designed, which can be installed on the rear of a mobile platform. An inverted pyramid-shaped weeding tool equipped in the modular weeder can shovel out weeds without being contaminated by soil. The weed detection and control method was implemented on an embedded system with a high-speed graphics processing unit and integrated with the weeder. The experimental results showed that even if the speed of the mobile platform reaches 20 cm/s, the weeds can still be accurately detected and the position of the weeds can be located by the system. Moreover, the weeding mechanism can successfully shovel out the roots of the weeds. The proposed weeder has been tested in the field, and its performance and weed coverage have been verified to be precise for weeding. Full article

(This article belongs to the Special Issue Design and Application of Agricultural Equipment in Tillage System)

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