Agriculture

21 pages, 1352 KiB

Open AccessEditor’s ChoiceArticle

The Role of Consumer-Citizens and Connectedness to Nature in the Sustainable Transition to Agroecological Food Systems: The Mediation of Innovative Business Models and a Multi-Level Perspective

by Sepide Mehrabi, Juan Carlos Perez-Mesa and Cynthia Giagnocavo

Agriculture 2022, 12(2), 203; https://doi.org/10.3390/agriculture12020203 - 31 Jan 2022

Cited by 23 | Viewed by 7619

Abstract

Conventional agricultural systems have contributed to social, economic and environmental problems and are the main threat to global sustainability. In response, theoretical frameworks to describe the transition to sustainable food systems have been proposed, emphasizing the necessity to shift from farm-level solutions to [...] Read more.

Conventional agricultural systems have contributed to social, economic and environmental problems and are the main threat to global sustainability. In response, theoretical frameworks to describe the transition to sustainable food systems have been proposed, emphasizing the necessity to shift from farm-level solutions to a focus on interactions within the entire value chain, from production to consumption. Despite the emphasis on the importance and potential of consumers to contribute to sustainable agri-food transitions, approaches to their role have remained within the traditional, linear supply chain framework. Marketing approaches view consumers as passive actors, limited to voting with their wallets, which has deepened the disconnection between consumers, producers and nature, resulting in a triple fracture. We analyze the role of the consumer in agri-food systems, contrasting marketing approaches with other consumers/citizens concepts and locate them within sustainability transition frameworks and a multi-level perspective. We discuss the re-establishment of the connection between farmers and consumers and human–nature connectedness and explore this connection mediated through innovative business models, which act as niche innovations with the capacity to influence regimes and landscapes within the multi-level perspective. The role of consumers/citizens in the co-creation of innovative business models is also addressed. Full article

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

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13 pages, 990 KiB

Open AccessEditor’s ChoiceArticle

Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning

by Józef Gorzelany, Justyna Belcar, Piotr Kuźniar, Gniewko Niedbała and Katarzyna Pentoś

Agriculture 2022, 12(2), 200; https://doi.org/10.3390/agriculture12020200 - 31 Jan 2022

Cited by 18 | Viewed by 2914

Abstract

The study investigated the selected mechanical properties of fresh and stored large cranberries. The analyses focused on changes in the energy requirement up to the breaking point and aimed to identify the apparent elasticity index of the fruit of the investigated large cranberry [...] Read more.

The study investigated the selected mechanical properties of fresh and stored large cranberries. The analyses focused on changes in the energy requirement up to the breaking point and aimed to identify the apparent elasticity index of the fruit of the investigated large cranberry fruit varieties relating to harvest time, water content, as well as storage duration and conditions. After 25 days in storage, the fruit of the investigated varieties were found with a decrease in mean acidity, from 1.56 g⋅100 g⁻¹ to 1.42 g⋅100 g⁻¹, and mean water content, from 89.71% to 87.95%. The findings showed a decrease in breaking energy; there was also a change in the apparent modulus of elasticity, its mean value in the fresh fruit was 0.431 ± 0.07 MPa, and after 25 days of storage it decreased to 0.271 ± 0.08 MPa. The relationships between the cranberry varieties, storage temperature, duration of storage, x, y, and z dimensions of the fruits, and their selected mechanical parameters were modeled with the use of multiple linear regression, artificial neural networks, and support vector machines. Machine learning techniques outperformed multiple linear regression. Full article

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

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20 pages, 493 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Consumers’ Benefit—Risk Perception on Pesticides and Food Safety—A Survey in Greece

by Konstantinos B. Simoglou and Emmanouil Roditakis

Agriculture 2022, 12(2), 192; https://doi.org/10.3390/agriculture12020192 - 30 Jan 2022

Cited by 20 | Viewed by 6122

Abstract

Pesticides are widely used to protect agricultural products from pests and diseases. Although a strict regulatory framework exists in the EU, concerns about pesticide residues in food are retained among consumers. This study represents the first large sample (N = 1846) attempt to [...] Read more.

Pesticides are widely used to protect agricultural products from pests and diseases. Although a strict regulatory framework exists in the EU, concerns about pesticide residues in food are retained among consumers. This study represents the first large sample (N = 1846) attempt to identify the main predictors influencing Greek consumers’ attitude concerning the benefits—risks ratio of pesticide use. After a principal components analysis and a bivariate logistic regression were performed, it was found that Greek consumers express high concerns from pesticide residues in food regarding their loved ones and their own health. At the same time, however, they recognize to a significant extent beneficial contributions of the use of pesticides to food security and the national economy, as well. Several significant predictors of consumer’s attitude towards benefit—risks perception of pesticide use was identified, concerning personal values, pesticide user status, gender, confidence in controlling and certification procedures, and received information. Our results suggest that efforts for risk communication are needed to address food safety issues targeting the general public. Full article

(This article belongs to the Topic New Trends in Agri-Food Sector: Environmental, Economic and Social Perspectives)

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15 pages, 1176 KiB

Open AccessEditor’s ChoiceArticle

Salt Tolerant Eggplant Rootstocks Modulate Sodium Partitioning in Tomato Scion and Improve Performance under Saline Conditions

by Satish Kumar Sanwal, Anita Mann, Arvind Kumar, Hari Kesh, Gurpreet Kaur, Arvind Kumar Rai, Raj Kumar, Parbodh C. Sharma, Ashwani Kumar, Anant Bahadur, Bijendra Singh and Pradeep Kumar

Agriculture 2022, 12(2), 183; https://doi.org/10.3390/agriculture12020183 - 27 Jan 2022

Cited by 20 | Viewed by 2966

Abstract

Grafting on salt tolerant eggplant rootstocks can be a promising approach for enhancing the salinity tolerance of tomato. In this study, the performance of tomato cv. Kashi Aman grafted on two salt tolerant eggplant rootstocks (IC-111056 and IC-354557) was evaluated against non-grafted control [...] Read more.

Grafting on salt tolerant eggplant rootstocks can be a promising approach for enhancing the salinity tolerance of tomato. In this study, the performance of tomato cv. Kashi Aman grafted on two salt tolerant eggplant rootstocks (IC-111056 and IC-354557) was evaluated against non-grafted control under saline (EC_iw 6 and 9 dS m⁻¹) and non-saline (EC_iw ~1 dS m⁻¹) irrigation for 2 years. Grafting improved tomato plant performance under salt stress. Moreover, rootstock IC-111056 outperformed IC-354557. An increase in the average fruit yield of grafted plants compared with non-grafted control at 6 and 9 dS m⁻¹ was 24.41% and 55.84%, respectively with rootstock IC-111056 and 20.25% and 49.08%, respectively with IC-354557. Grafted plants maintained a superior water status under saline irrigation, evidenced with the relative water content and chlorophyll SPAD index, along with higher proline and antioxidant enzyme activities (superoxide dismutase, catalase, and ascorbate peroxidase). Rootstocks mediated the partitioning of toxic saline ions in the scions by promoting higher Na⁺ accumulation (14% of mean accumulation) in the older leaves and lower (24%) in the younger leaves of grafted plants. This resulted in higher K⁺/Na⁺ ratios within the younger (active) leaves of the grafted plants. Our study demonstrates that grafting tomato seedlings on selected salt tolerant eggplant rootstocks is a viable alternative for improving plant physiological status and fruit yield under salt stress, through favorable modulation of salt ion partitioning in the scions. Full article

(This article belongs to the Special Issue Recent Advances in Horticultural Grafting)

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19 pages, 6055 KiB

Open AccessEditor’s ChoiceArticle

Comparative Evaluation of Land Surface Temperature Images from Unmanned Aerial Vehicle and Satellite Observation for Agricultural Areas Using In Situ Data

by Muhammad Awais, Wei Li, Sajjad Hussain, Muhammad Jehanzeb Masud Cheema, Weiguo Li, Rui Song and Chenchen Liu

Agriculture 2022, 12(2), 184; https://doi.org/10.3390/agriculture12020184 - 27 Jan 2022

Cited by 18 | Viewed by 3183

Abstract

Remotely-sensed data are a source of rich information and are valuable for precision agricultural tasks such as soil quality, plant disease analysis, crop stress assessment, and allowing for better management. It is necessary to validate the accuracy of land surface temperature (LST) that [...] Read more.

Remotely-sensed data are a source of rich information and are valuable for precision agricultural tasks such as soil quality, plant disease analysis, crop stress assessment, and allowing for better management. It is necessary to validate the accuracy of land surface temperature (LST) that is acquired from an unmanned aerial vehicle (UAV) and satellite-based remote sensing and verify these data by a comparison with in situ LST. Comprehensive studies at the field scale are still needed to understand the suitability of UAV imagery and resolution, for which ground measurement is used as a reference. In this study, we examined the accuracy of surface temperature data that were obtained from a thermal infrared (TIR) sensor placed on a UAV. Accordingly, we evaluated the LST from the Landsat 8 satellite for the same specific periods. We used contact thermometers to measure LSTs in situ for comparison and evaluation. Between 18 August and 2 September 2020, UAV imagery and in situ measurements were carried out. The effectiveness of high-resolution UAVs imagery and of Landsat 8 imagery was evaluated by considering a regression and correlation coefficient analysis. The data from the satellite photography was compared to the UAV imagery using statistical metrics after it had been pre-processed. Ground control points (GCPs) were collected to create a rigorous geo-referenced dataset of UAV imagery that could be compared to the geo-referenced satellite and aerial imagery. The UAV TIR LST showed higher accuracy (R² 0.89, 0.90, root-mean-square error (RMSE) 1.07, 0.70 °C) than the Landsat LST accuracy (R² 0.70, 0.73, (RMSE) 0.78 °C). The relationship between LST and the available soil water content (SWC) was also observed. The results suggested that the UAV-SMC correlation was negative (−0.85) for the image of DOY 230, while this value remains approximately constant (−0.86) for the DOY 245. Our results showed that satellite imagery that was coherent and correlated with UAV images could be useful to assess the general conditions of the field while the UAV favors localized circumscribed areas that the lowest resolution of satellites missed. Accordingly, our results could help with urban area and environmental planning decisions that take into account the thermal environment. Full article

(This article belongs to the Special Issue Remote-Sensing-Based Technologies for Crop Monitoring)

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23 pages, 4931 KiB

Open AccessEditor’s ChoiceArticle

The Role of Water Relations and Oxidative Stress in the Vase Life Response to Prolonged Storage: A Case Study in Chrysanthemum

by Dimitrios Fanourakis, Vassilis M. Papadakis, Evangelos Psyllakis, Vasileios A. Tzanakakis and Panayiotis A. Nektarios

Agriculture 2022, 12(2), 185; https://doi.org/10.3390/agriculture12020185 - 27 Jan 2022

Cited by 19 | Viewed by 3344

Abstract

Long storage periods have been associated with decreased vase life. In this study, the processes underlying the vase life response to prolonged storage were investigated, along with the potential of light reflectance profiles to estimate storage duration. Three cut chrysanthemum cultivars were exposed [...] Read more.

Long storage periods have been associated with decreased vase life. In this study, the processes underlying the vase life response to prolonged storage were investigated, along with the potential of light reflectance profiles to estimate storage duration. Three cut chrysanthemum cultivars were exposed to four cold (5 °C) storage periods (0, 7, 14, and 21 d). Stomata were present on the leaves (either side) and stem, but not on petals. As compared to the leaves, stomata on the stem were non-functional, smaller, and less dense. Floral transpiration was a small portion of the whole-cut flower transpiration, with the major contributor being the leaves or stem depending on the cut flower hydration. Storage duration linearly decreased vase life, with the rate of decrease being cultivar specific. Storage duration (0–21 d) did not affect leaf stomatal functioning, non-leaf tissue transpiration, or the relative contribution of each organ to the whole-cut flower transpiration. Cut flower hydration was generally enhanced by storage, while water uptake restoration ability was not impaired. Membrane lipid oxidation increased in response to storage duration owing to enhanced H₂O₂ accumulation. A strong correlation between membrane lipid oxidation level and the vase life response to cold storage was apparent. By examining the light reflectance profiles (400–1050 nm) of leaves (either side) and flowers (top view), an indication of the storage period could not be deduced. In conclusion, cultivar differences in vase life response to cold storage were attributed to variation in oxidative state, whereas cut flower water relations are clearly not involved. Full article

(This article belongs to the Special Issue Postharvest Storage of Agricultural Products)

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29 pages, 4738 KiB

Open AccessEditor’s ChoiceArticle

Assessing the Agricultural Sector’s Resilience to the 2008 Economic Crisis: The Case of Greece

by Labros Sdrolias, Anastasios Semos, Konstantinos Mattas, Efthimia Tsakiridou, Anastasios Michailides, Maria Partalidou and Dimitrios Tsiotas

Agriculture 2022, 12(2), 174; https://doi.org/10.3390/agriculture12020174 - 26 Jan 2022

Cited by 12 | Viewed by 2641

Abstract

This paper studies the resilience of the agricultural sector compared to eight other sectors of the Greek economy. The analysis is based on a multilevel methodological framework aiming to integrate equilibrium and evolutionary approaches by incorporating temporal (recovery and adaptability), geographical (regional resilience), [...] Read more.

This paper studies the resilience of the agricultural sector compared to eight other sectors of the Greek economy. The analysis is based on a multilevel methodological framework aiming to integrate equilibrium and evolutionary approaches by incorporating temporal (recovery and adaptability), geographical (regional resilience), and sectorial (industrial resilience) aspects, quantified concerning the 2008 economic crisis. Within this composite context, resilience is measured on GVA data according to a dual-axis: horizontally, in terms of measuring the recovering time or the time of transition to a new state of functionality due to a shock, and vertically, in terms of capturing the variability caused by the shock, approximating the system’s adaptability. The analysis shows that the agricultural sector in Greece is generally resilient; although, it has not retained its pre-crisis maximum performance, and it has the smallest Gross Value Added magnitude and the most uneven distribution across the regional dimension. Overall, the analysis promotes the methodological conceptualization of regional resilience and provides insights into the case study of the structural analysis of the Greek rural economy. Full article

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

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13 pages, 4236 KiB

Open AccessEditor’s ChoiceArticle

Research on Dynamic Load Characteristics of Advanced Variable Speed Drive System for Agricultural Machinery during Engagement

by Zhun Cheng and Zhixiong Lu

Agriculture 2022, 12(2), 161; https://doi.org/10.3390/agriculture12020161 - 24 Jan 2022

Cited by 11 | Viewed by 2381

Abstract

A wet clutch is the key component to realize power uninterrupted in agricultural machinery operation. To reduce impact of the system and improve engagement quality, this paper studies and establishes the dynamic load characteristics model of a wet clutch and analyzes three kinds [...] Read more.

A wet clutch is the key component to realize power uninterrupted in agricultural machinery operation. To reduce impact of the system and improve engagement quality, this paper studies and establishes the dynamic load characteristics model of a wet clutch and analyzes three kinds of tractor working conditions. This paper proposes and adopts the method of combining ‘PLS analysis-Improved SA—Comparison of various models-Actual test data’. The results show that with the limit of 100 Nm, the relationship between dynamic load characteristics and oil pressure is opposite. Load is highly inversely correlated with dynamic load, and it has enough precision to build a power curve model only by load (MAPE is 4.5929%). Take a certain type of tractor for example, oil pressure should be maintained at a low level, plowing resistance should be greater than 1600 N and the mass of transportation should avoid 600~1800 kg. This study provides a direct basis for the control, design and performance improvement of agricultural machinery. Full article

(This article belongs to the Special Issue Applications of Electric, Electronics, and Computer Science to Agricultural Machines)

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20 pages, 2420 KiB

Open AccessEditor’s ChoiceArticle

Analysis on Regional Differences and Spatial Convergence of Digital Village Development Level: Theory and Evidence from China

by Xiaojing Li, Raj Bahadur Singh Chandel and Xianli Xia

Agriculture 2022, 12(2), 164; https://doi.org/10.3390/agriculture12020164 - 24 Jan 2022

Cited by 13 | Viewed by 3351

Abstract

In the context of the rapid development of the global Internet, evaluating the digital village development level (DVI) in different regions is important for policy formulation and scientific research in the field of rural digital development. Based on the measurement of DVI using [...] Read more.

In the context of the rapid development of the global Internet, evaluating the digital village development level (DVI) in different regions is important for policy formulation and scientific research in the field of rural digital development. Based on the measurement of DVI using panel data of provinces from 2005 to 2018, we also explored the causes of DVI development differences using the Dagum Gini coefficient decomposition and spatial convergence method. The study found that: (1) During the whole sample period, the DVI in China and the four major regions showed a significant upward trend as a whole, with the highest in the eastern region and the lowest in the western region, but the development of DVI among different provinces showed regional non-equilibrium characteristics. (2) DVI has a typical agglomeration effect in the spatial and geographical distribution, and regional differences are the primary source of differences in DVI development in China, but the overall regional differences show a decreasing trend. (3) Except for the northeast region,

σ

-convergence exists in the other three regions; in addition, all regions have significant conditional

β

-convergence, but the convergence speed is significantly different. (4) Rural population density, industrial structure, and economic development level significantly affect DVI. Therefore, we suggest strengthening the inter-regional connection and element flow and realizing the improvement of the overall DVI of the region through cooperation and mutual assistance. Full article

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

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15 pages, 1167 KiB

Open AccessEditor’s ChoiceArticle

Effect of Silicon-Containing Fertilizers on the Nutritional Value of Grass–Legume Mixtures on Temporary Grasslands

by Barbara Borawska-Jarmułowicz, Grażyna Mastalerczuk, Maria Janicka and Barbara Wróbel

Agriculture 2022, 12(2), 145; https://doi.org/10.3390/agriculture12020145 - 21 Jan 2022

Cited by 13 | Viewed by 2062

Abstract

The study was conducted to evaluate the influence of silicon application on the botanical composition of grass–legume mixtures as well as the nutritional value of individual species and mixtures. The two-factor field experiment was established in a split-block arrangement. The two grass–legume mixtures, [...] Read more.

The study was conducted to evaluate the influence of silicon application on the botanical composition of grass–legume mixtures as well as the nutritional value of individual species and mixtures. The two-factor field experiment was established in a split-block arrangement. The two grass–legume mixtures, consisting of Dactylis glomerata, Festulolium braunii and Trifolium pratense or Medicago x varia and grass mixture—Dactylis glomerata, Festulolium braunii and Lolium perenne, were sown on arable land. Silicon foliar and mineral NPK fertilization was applied in three variants: NPK (non-Si-fertilized), NPK+Si (Herbagreen), NPK+Si (Optysil). Changes in the botanical composition in the sward of mixtures during the study period were influenced by the competitiveness of individual plants and weather conditions, whereas the effect of foliar fertilization with silicon was only slightly marked. The nutritional value of individual species of grasses and legumes varied over the years and also depended on the application of silicon. The greatest content of crude protein (CP), irrespective of the species and variant of silicon fertilization, was noticed in years with no drought. Our research confirmed the beneficial effect of silicon fertilization, independent of weather conditions, on the CP content and on the reduction of crude fibre (CF), neutral detergent fibre (NDF) and acid detergent fibre (ADF) in plants, especially when Herbagreen was applied. The nutritional value of the mixtures under applied silicon fertilization depended on botanical composition. In the conditions of low rainfall, a large share of F. braunii and an approximately 20% contribution of T. pratense resulted in a higher content of CP. These results may be used in the fertilization practices of temporary grassland for enhancing nutritional value, especially crude protein content, and on the reduction of fibre in the sward. Full article

(This article belongs to the Section Crop Production)

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30 pages, 2563 KiB

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 24 | Viewed by 6304

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 KiB

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 21 | Viewed by 3236

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 KiB

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 15 | Viewed by 4096

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 KiB

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 39 | Viewed by 3047

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 Digital Agriculture)

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16 pages, 4414 KiB

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 - 06 Jan 2022

Cited by 22 | Viewed by 2988

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 KiB

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 - 04 Jan 2022

Cited by 32 | Viewed by 4563

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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18 pages, 1032 KiB

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 - 01 Jan 2022

Cited by 20 | Viewed by 5439

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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14 pages, 2907 KiB

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 - 01 Jan 2022

Cited by 24 | Viewed by 2027

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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15 pages, 2682 KiB

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 21 | Viewed by 2274

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 KiB

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 15 | Viewed by 3650

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 KiB

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 14 | Viewed by 2495

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 KiB

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 51 | Viewed by 15144

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 KiB

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 10 | Viewed by 3515

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 KiB

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 12 | Viewed by 4984

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 KiB

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 16 | Viewed by 3648

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 KiB

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 18 | Viewed by 2538

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 KiB

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 13 | Viewed by 3704

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 KiB

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 29 | Viewed by 3234

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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11 pages, 397 KiB

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 - 09 Dec 2021

Cited by 35 | Viewed by 5040

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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25 pages, 920 KiB

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 - 09 Dec 2021

Cited by 22 | Viewed by 4830

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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17 pages, 6820 KiB

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 - 08 Dec 2021

Cited by 14 | Viewed by 2904

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 KiB

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 - 08 Dec 2021

Cited by 14 | Viewed by 3641

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 KiB

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 - 08 Dec 2021

Cited by 16 | Viewed by 4046

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 KiB

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 - 07 Dec 2021

Cited by 20 | Viewed by 3329

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 KiB

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 - 03 Dec 2021

Cited by 13 | Viewed by 2675

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 KiB

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 - 02 Dec 2021

Cited by 10 | Viewed by 2184

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 KiB

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 32 | Viewed by 9046

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 KiB

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 12 | Viewed by 2425

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 KiB

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 13 | Viewed by 6383

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 KiB

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 13 | Viewed by 3211

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 KiB

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 12 | Viewed by 2889

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 KiB

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 15 | Viewed by 3848

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 KiB

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 11 | Viewed by 2523

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 KiB

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 13 | Viewed by 3213

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 KiB

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 15 | Viewed by 5227

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 KiB

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 16 | Viewed by 3777

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 KiB

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 18 | Viewed by 3041

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 KiB

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 - 09 Nov 2021

Cited by 13 | Viewed by 2410

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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17 pages, 634 KiB

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 - 06 Nov 2021

Cited by 12 | Viewed by 3606

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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19 pages, 6252 KiB

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 - 06 Nov 2021

Cited by 18 | Viewed by 2619

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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