Journal Description
Agriculture
Agriculture
is an international, scientific peer-reviewed open access journal published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubAg, AGRIS, RePEc, and other databases.
- Journal Rank: JCR - Q1 (Agronomy) / CiteScore - Q2 (Plant Science)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.7 days after submission; acceptance to publication is undertaken in 2.4 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Agriculture include: Poultry, Grasses and Crops.
Impact Factor:
3.6 (2022);
5-Year Impact Factor:
3.6 (2022)
Latest Articles
Assessing Methane Emissions from Rice Fields in Large Irrigation Projects Using Satellite-Derived Land Surface Temperature and Agronomic Flooding: A Spatial Analysis
Agriculture 2024, 14(3), 496; https://doi.org/10.3390/agriculture14030496 - 19 Mar 2024
Abstract
Synthetic aperture radar (SAR) imagery, notably Sentinel-1A’s C-band, VV, and VH polarized SAR, has emerged as a crucial tool for mapping rice fields, especially in regions where cloud cover hinders optical imagery. Employing multi-temporal characteristics, SAR data were regularly collected and parameterized using
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Synthetic aperture radar (SAR) imagery, notably Sentinel-1A’s C-band, VV, and VH polarized SAR, has emerged as a crucial tool for mapping rice fields, especially in regions where cloud cover hinders optical imagery. Employing multi-temporal characteristics, SAR data were regularly collected and parameterized using MAPscape-Rice software, which integrates a fully automated processing chain to convert the data into terrain-geocoded σ° values. This facilitated the generation of rice area maps through a rule-based classifier approach, with classification accuracies ranging from 88.5 to 91.5 and 87.5 percent in 2017, 2018, and 2022, respectively. To estimate methane emissions, IPCC (37.13 kg/ha/season, 42.10 kg/ha/season, 43.19 kg/ha/season) and LST (36.05 kg/ha/season, 41.44 kg/ha/season, 38.07 kg/ha/season) factors were utilized in 2017, 2018 and 2022. Total methane emissions were recorded as 19.813 Gg, 20.661 Gg, and 25.72 Gg using IPCC and 19.155 Gg, 20.373 Gg, and 22.76 Gg using LST factors in 2017, 2018 and 2022. Overall accuracy in methane emission estimation, assessed against field observations, ranged from (IPCC) 85.71, 91.32, and 80.25 percent to (LST) 83.69, 91.43, and 84.69 percent for the years 2017, 2018 and 2022, respectively, confirming the efficacy of remote sensing in greenhouse gas monitoring and its potential for evaluating the impact of large-scale water management strategies on methane emissions and carbon credit-based ecosystem services at regional or national levels.
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(This article belongs to the Special Issue Greenhouse Gas Emissions in Agricultural System and Green Infrastructures: Mechanisms and Mitigation Measures)
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Open AccessArticle
A Glimpse into the Genetic Heritage of the Olive Tree in Malta
by
Monica Marilena Miazzi, Antonella Pasqualone, Marion Zammit-Mangion, Michele Antonio Savoia, Valentina Fanelli, Silvia Procino, Susanna Gadaleta, Francesco Luigi Aurelio and Cinzia Montemurro
Agriculture 2024, 14(3), 495; https://doi.org/10.3390/agriculture14030495 - 18 Mar 2024
Abstract
The genetic diversity of the ancient autochthonous olive trees on the Maltese islands and the relationship with the wild forms growing in marginal areas of the island (57 samples), as well as with the most widespread cultivars in the Mediterranean region (150 references),
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The genetic diversity of the ancient autochthonous olive trees on the Maltese islands and the relationship with the wild forms growing in marginal areas of the island (57 samples), as well as with the most widespread cultivars in the Mediterranean region (150 references), were investigated by genetic analysis with 10 SSR markers. The analysis revealed a high genetic diversity of Maltese germplasm, totaling 84 alleles and a Shannon information index (I) of 1.08. All samples from the upper and the lower part of the crown of the Bidni trees belonged to the same genotype, suggesting that there was no secondary top-grafting of the branches. The Bidni trees showed close relationships with the local wild germplasm, suggesting that the oleaster population played a role in the selection of the Bidni variety. Genetic similarities were also found between Maltese cultivars and several Italian varieties including accessions putatively resistant to the bacterium Xylella fastidiosa, which has recently emerged in the Apulia region (Italy) and has caused severe epidemics on olive trees over the last decade.
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(This article belongs to the Topic Mediterranean Biodiversity)
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Study on the Hole-Forming Performance and Opening of Mulching Film for a Dibble-Type Transplanting Device
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Xiaoshun Zhao, Zhuangzhuang Hou, Jizong Zhang, Huali Yu, Jianjun Hao and Yuhua Liu
Agriculture 2024, 14(3), 494; https://doi.org/10.3390/agriculture14030494 - 18 Mar 2024
Abstract
In order to improve the quality of transplanting devices and solve the problems of the poor effect on soil moisture conservation and more weeds easily growing due to the high mulching-film damage rate with an excessive number of hole openings, we developed a
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In order to improve the quality of transplanting devices and solve the problems of the poor effect on soil moisture conservation and more weeds easily growing due to the high mulching-film damage rate with an excessive number of hole openings, we developed a dibble-type transplanting device consisting of a dibble-type transplanting unit, a transplanting disc, and a dibble axis. The ADAMS software Adams2020 (64bit) was used to simulate and analyze the kinematic track of the transplanting device. The results of the analysis show that, when the hole opening of the envelope in the longitudinal dimension was the smallest, the transplanting characteristic coefficient was 1.034, the transplanting angle was 95°, and the transplanting frequency had no influence. With the help of the ANSYS WORKBENCH software Ansys19.2 (64bit), an analysis of the process of the formation of an opening in the mulching film and a mechanical simulation of this process were completed. The results indicate that, when the maximum shear stress of the mulching film was the smallest, the transplanting characteristic coefficient was 1.000, the transplanting frequency was 36 plants·min−1, and the transplanting angle was 95°. In addition, the device was tested in a film-breaking experiment on a soil-tank test bench to verify the hole opening in the mulching film. The bench test showed that, when the longitudinal dimension was the smallest, the transplanting characteristic coefficient was 1.034, the transplanting frequency was 36 plants·min−1, and the transplanting angle was 95°. When the lateral dimension was the smallest, the transplanting characteristic coefficient was 1.034, the transplanting frequency was 36 plants·min−1, and the transplanting angle was 90°. The theoretical analysis, kinematic simulation, and soil-tank test results were consistent, verifying the validity and ensuring the feasibility of the transplanting device. This study provides a reference for the development of transplanting devices.
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(This article belongs to the Special Issue Design, Optimization and Analysis of Agricultural Machinery)
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Efficiency Factors in the Olive Oil Sector in Turkey
by
Yousuf Abdulmunem Noman and Domingo Fernández Uclés
Agriculture 2024, 14(3), 493; https://doi.org/10.3390/agriculture14030493 - 18 Mar 2024
Abstract
Turkey ranks among the top five olive oil-producing countries in the world, and the olive crop plays a crucial role in its economy, economically, environmentally, and socially. One of the primary challenges facing the agricultural sector is its profitability. Therefore, the aim of
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Turkey ranks among the top five olive oil-producing countries in the world, and the olive crop plays a crucial role in its economy, economically, environmentally, and socially. One of the primary challenges facing the agricultural sector is its profitability. Therefore, the aim of this study is to analyse the olive sector in terms of economic efficiency, to identify productive and organizational variables directly associated with higher economic efficiency. Data were obtained from 193 organizations in the sector. A dual methodology is employed, comprising Data Envelopment Analysis (DEA) and, subsequently, Qualitative Comparative Analysis (QCA). The findings highlight the relevance of variables such as organization size, irrigation usage, focus on olive oil, or cultivation on sloping terrain as factors associated with a higher level of economic efficiency.
Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
Open AccessArticle
Analysis and Structural Optimization Test on the Collision Mechanical Model of Blade Jun-Cao Grinding Hammer
by
Shuhe Zheng, Chongcheng Chen and Yuming Guo
Agriculture 2024, 14(3), 492; https://doi.org/10.3390/agriculture14030492 - 18 Mar 2024
Abstract
Aiming at the problems found in grinding Jun-Cao, such as poor grinding effect and high grinding power of mill, this study proposes a blade Jun-Cao grinding hammer based on the traditional hammer mill. With dynamics model analysis, it had better performance than a
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Aiming at the problems found in grinding Jun-Cao, such as poor grinding effect and high grinding power of mill, this study proposes a blade Jun-Cao grinding hammer based on the traditional hammer mill. With dynamics model analysis, it had better performance than a traditional hammer. By simulating the operation process in the DEM, forces on Jun-Cao and their motions were analyzed. By optimizing the structural parameters of the hammer blade based on multiobjective optimization using the genetic algorithm, an optimal solution set was obtained as a reference for practical production. Meanwhile, a bench test was designed to compare the traditional rectangular hammer with the new blade hammer regarding the operation effect. The result proved the following: (1) cutting edge length, cutting edge thickness and hammer thickness had a significant influence on the grinding effect and grinding power; (2) a total of 22 optimal solution sets were obtained, based on which the blade hammer with a cutting edge length of 45 mm, a cutting edge thickness of 3 mm and a hammer thickness of 7 mm was finally selected in the bench test; (3) the bench test proved that the blade hammer was generally superior to the traditional rectangular hammer with the output per kilowatt-hour having been improved by 13.55% on average.
Full article
(This article belongs to the Section Agricultural Technology)
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Yield Gap Analysis of Super High-Yielding Rice (>15 t ha−1) in Two Ecological Regions
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Zhongwei Wei, Yuzhu Zhang and Wenyu Jin
Agriculture 2024, 14(3), 491; https://doi.org/10.3390/agriculture14030491 - 18 Mar 2024
Abstract
Super high-yielding rice (SHYR) (>15 t ha−1) plays a crucial role in global food production and security. We hypothesized that the external environment of different ecological regions could improve biomass accumulation in different periods and thus increase the rice yield. Two
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Super high-yielding rice (SHYR) (>15 t ha−1) plays a crucial role in global food production and security. We hypothesized that the external environment of different ecological regions could improve biomass accumulation in different periods and thus increase the rice yield. Two SHYR varieties, i.e., Xiangliangyou900 (XLY900) and Yliangyou900 (YLY900), were cultivated in the YONGSHENG and LONGHUI ecoregions, China. The results indicated that the average yield of the two SHYRs in the LONGHUI ecological region was 15.27–15.45 t ha−1 and 18.81–20.10 t ha−1 in YONGSHENG. The high grain yield in the YONGSHENG ecoregion was mainly due to the increased number of spikelets per panicle, crop growth rate, and total biomass during the transplanting–heading stage (TP-HS) and heading–maturity stage (HS-MS), and harvest index. The yield of SHYR was significantly correlated with external environment conditions, i.e., average minimum temperature, average daytime, and night-time temperature, and average daily temperature at the TP-HS, HS-MS, and transplanting–maturity (TP-MS) stages. The rice yield was significantly and positively correlated with the cumulative daily radiation. Therefore, it can be concluded that the final yield of super high-yield rice is closely related to the utilization of temperature and radiation resources during the growth process in the ecological environment.
Full article
(This article belongs to the Special Issue Enhancing Yield and Quality in Conventional and New Crops: From Molecular Approaches to Agricultural Practices)
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Enhancing Fruit Fly Detection in Complex Backgrounds Using Transformer Architecture with Step Attention Mechanism
by
Lexin Zhang, Kuiheng Chen, Liping Zheng, Xuwei Liao, Feiyu Lu, Yilun Li, Yuzhuo Cui, Yaze Wu, Yihong Song and Shuo Yan
Agriculture 2024, 14(3), 490; https://doi.org/10.3390/agriculture14030490 - 18 Mar 2024
Abstract
This study introduces a novel high-accuracy fruit fly detection model based on the Transformer structure, specifically aimed at addressing the unique challenges in fruit fly detection such as identification of small targets and accurate localization against complex backgrounds. By integrating a step attention
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This study introduces a novel high-accuracy fruit fly detection model based on the Transformer structure, specifically aimed at addressing the unique challenges in fruit fly detection such as identification of small targets and accurate localization against complex backgrounds. By integrating a step attention mechanism and a cross-loss function, this model significantly enhances the recognition and localization of fruit flies within complex backgrounds, particularly improving the model’s effectiveness in handling small-sized targets and its adaptability under varying environmental conditions. Experimental results demonstrate that the model achieves a precision of 0.96, a recall rate of 0.95, an accuracy of 0.95, and an F1-score of 0.95 on the fruit fly detection task, significantly outperforming leading object detection models such as YOLOv8 and DETR. Specifically, this research delves into and optimizes for challenges faced in fruit fly detection, such as recognition issues under significant light variation, small target size, and complex backgrounds. Through ablation experiments comparing different data augmentation techniques and model configurations, the critical contributions of the step attention mechanism and cross-loss function to enhancing model performance under these complex conditions are further validated. These achievements not only highlight the innovativeness and effectiveness of the proposed method, but also provide robust technical support for solving practical fruit fly detection problems in real-world applications, paving new paths for future research in object detection technology.
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(This article belongs to the Section Digital Agriculture)
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An Integrated IoT Sensor-Camera System toward Leveraging Edge Computing for Smart Greenhouse Mushroom Cultivation
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Hoang Hai Nguyen, Dae-Yun Shin, Woo-Sung Jung, Tae-Yeol Kim and Dae-Hyun Lee
Agriculture 2024, 14(3), 489; https://doi.org/10.3390/agriculture14030489 - 18 Mar 2024
Abstract
Industrial greenhouse mushroom cultivation is currently promising, due to the nutritious and commercial mushroom benefits and its convenience in adapting smart agriculture technologies. Traditional Device-Cloud protocol in smart agriculture wastes network resources when big data from Internet of Things (IoT) devices are directly
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Industrial greenhouse mushroom cultivation is currently promising, due to the nutritious and commercial mushroom benefits and its convenience in adapting smart agriculture technologies. Traditional Device-Cloud protocol in smart agriculture wastes network resources when big data from Internet of Things (IoT) devices are directly transmitted to the cloud server without processing, delaying network connection and increasing costs. Edge computing has emerged to bridge these gaps by shifting partial data storage and computation capability from the cloud server to edge devices. However, selecting which tasks can be applied in edge computing depends on user-specific demands, suggesting the necessity to design a suitable Smart Agriculture Information System (SAIS) architecture for single-crop requirements. This study aims to design and implement a cost-saving multilayered SAIS architecture customized for smart greenhouse mushroom cultivation toward leveraging edge computing. A three-layer SAIS adopting the Device-Edge-Cloud protocol, which enables the integration of key environmental parameter data collected from the IoT sensor and RGB images collected from the camera, was tested in this research. Implementation of this designed SAIS architecture with typical examples of mushroom cultivation indicated that low-cost data pre-processing procedures including small-data storage, temporal resampling-based data reduction, and lightweight artificial intelligence (AI)-based data quality control (for anomalous environmental conditions detection) together with real-time AI model deployment (for mushroom detection) are compatible with edge computing. Integrating the Edge Layer as the center of the traditional protocol can significantly save network resources and operational costs by reducing unnecessary data sent from the device to the cloud, while keeping sufficient information.
Full article
(This article belongs to the Special Issue Application of Intelligent Greenhouse and Plant Factory Systems in Agricultural Production)
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Progress in Research and Prospects for Application of Precision Gene-Editing Technology Based on CRISPR–Cas9 in the Genetic Improvement of Sheep and Goats
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Zeyu Lu, Lingtian Zhang, Qing Mu, Junyang Liu, Yu Chen, Haoyuan Wang, Yanjun Zhang, Rui Su, Ruijun Wang, Zhiying Wang, Qi Lv, Zhihong Liu, Jiasen Liu, Yunhua Li and Yanhong Zhao
Agriculture 2024, 14(3), 487; https://doi.org/10.3390/agriculture14030487 - 18 Mar 2024
Abstract
Due to recent innovations in gene editing technology, great progress has been made in livestock breeding, with researchers rearing gene-edited pigs, cattle, sheep, and other livestock. Gene-editing technology involves knocking in, knocking out, deleting, inhibiting, activating, or replacing specific bases of DNA or
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Due to recent innovations in gene editing technology, great progress has been made in livestock breeding, with researchers rearing gene-edited pigs, cattle, sheep, and other livestock. Gene-editing technology involves knocking in, knocking out, deleting, inhibiting, activating, or replacing specific bases of DNA or RNA sequences at the genome level for accurate modification, and such processes can edit genes at a fixed point without needing DNA templates. In recent years, although clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 system-mediated gene-editing technology has been widely used in research into the genetic breeding of animals, the system’s efficiency at inserting foreign genes is not high enough, and there are certain off-target effects; thus, it is not appropriate for use in the genome editing of large livestock such as cashmere goats. In this study, the development status, associated challenges, application prospects, and future prospects of CRISPR/Cas9-mediated precision gene-editing technology for use in livestock breeding were reviewed to provide a theoretical reference for livestock gene function analysis, genetic improvement, and livestock breeding that account for characteristics of local economies.
Full article
(This article belongs to the Topic Application of Reproductive and Genomic Biotechnologies for Livestock Breeding and Selection)
Open AccessArticle
Design and Experiment of Automatic Transport System for Planting Plate in Plant Factory
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Dongdong Jia, Wenzhong Guo, Lichun Wang, Wengang Zheng and Guohua Gao
Agriculture 2024, 14(3), 488; https://doi.org/10.3390/agriculture14030488 - 17 Mar 2024
Abstract
In the plant factories using stereoscopic cultivation systems, the cultivation plate transport equipment is an essential component of production. However, there are problems, such as high labor intensity, low levels of automation, and poor versatility of existing solutions, that can affect the efficiency
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In the plant factories using stereoscopic cultivation systems, the cultivation plate transport equipment is an essential component of production. However, there are problems, such as high labor intensity, low levels of automation, and poor versatility of existing solutions, that can affect the efficiency of cultivation plate transport processes. To address these issues, this study designed a cultivation plate transport system that can automatically input and output cultivation plates, and can flexibly adjust its structure to accommodate different cultivation frame heights. We elucidated the working principles of the transport system and carried out structural design and parameter calculation for the lift cart, input actuator, and output actuator. In the input process, we used dynamic simulation technology to obtain an optimum propulsion speed of 0.3 m·s−1. In the output process, we used finite element numerical simulation technology to verify that the deformation of the cultivation plate and the maximum stress suffered by it could meet the operational requirements. Finally, operation and performance experiments showed that, under the condition of satisfying the allowable amount of positioning error in the horizontal and vertical directions, the horizontal operation speed was 0.2 m·s−1, the maximum positioning error was 2.87 mm, the vertical operation speed was 0.3 m·s−1, and the maximum positioning error was 1.34 mm. Accordingly, the success rate of the transport system was 92.5–96.0%, and the operational efficiency was 176–317 plates/h. These results proved that the transport system could meet the operational requirements and provide feasible solutions for the automation of plant factory transport equipment.
Full article
(This article belongs to the Special Issue Application of Modern Agricultural Equipment in Crop Cultivation)
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Analysis of the Physico-Chemical Properties of Bean Seeds after Three Years of Digestate Use
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Milan Koszel, Stanisław Parafiniuk, Sławomir Kocira, Andrzej Bochniak, Artur Przywara, Edmund Lorencowicz, Pavol Findura and Atanas Zdravkov Atanasov
Agriculture 2024, 14(3), 486; https://doi.org/10.3390/agriculture14030486 - 16 Mar 2024
Abstract
Taking into consideration its physico-chemical properties, digestate should be used primarily as a fertiliser. The possible ways of using digestate as a fertiliser in agriculture were identified, and digestate collected from an agricultural biogas plant was tested for its macroelement and heavy metal
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Taking into consideration its physico-chemical properties, digestate should be used primarily as a fertiliser. The possible ways of using digestate as a fertiliser in agriculture were identified, and digestate collected from an agricultural biogas plant was tested for its macroelement and heavy metal content. The research was conducted on Haplic LUVISOLS soil according FAO classification. The area of the land plots was 75 m2. All measurements were carried out in ten replicates. Seed yield was determined at 2.6 t ha−1. The thousand-seed weight was similar in the three growing seasons, and averaged 171.49 g to 184.44 g for the three years under analysis. For the control object, the average thousand-seed weight from the three years of the experiment was 168.56 g. This parameter was significantly influenced by the year of analysis. The highest protein content was obtained in 2022 (an average of 20.3%), which was significantly higher than in 2021 (20.13%) and 2020 (20.12%). The analysis showed an increase in the average value for the three harvest years regarding the fat content of the multiflora bean seeds depending on the post-harvest digestate dose, ranging from 0.47% to 0.61%. In the control object, the average fat content for the three harvest years under analysis was 0.41%. The year under analysis had no significant impact on fat content. A positive correlation was found between the digestate dose and protein, fat, and carbohydrate contents per 100 g of beans. Increasing the dose resulted in statistically significant differences from the lower dose. The obtained results show an increase in macroelement content depending on the digestate dose applied. The average carbohydrate content per 100 g of beans for the three years under analysis ranged from 49.78 g to 54.01 g, while the calcium content per 100 g of beans ranged from 109.23 mg to 124.00 mg. In contrast, the magnesium content in 100 g of bean ranged from 129.91 g to 137.01 mg, the phosphorus content in 100 g of bean from 366.99 mg to 387.00 mg, and the potassium content in 100 g of bean from 1341.20 mg to 1394.06 mg. Statistical analysis revealed statistically significant differences except for potassium, where no differences were found for the two highest doses. In addition, no differences were found in the average phosphorus and potassium content between the years under analysis. The study showed an increase in yield depending on the amount of digestate applied. The highest dose used in the experiment provided the most nitrogen and macronutrients, with a positive effect on yield velocity, protein and fat content, micronutrients, and macronutrients in beans.
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(This article belongs to the Special Issue Efficient Use of Irrigation and Fertilizer to Increase Crop Yield)
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An Apple Detection and Localization Method for Automated Harvesting under Adverse Light Conditions
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Guoyu Zhang, Ye Tian, Wenhan Yin and Change Zheng
Agriculture 2024, 14(3), 485; https://doi.org/10.3390/agriculture14030485 - 16 Mar 2024
Abstract
The use of automation technology in agriculture has become particularly important as global agriculture is challenged by labor shortages and efficiency gains. The automated process for harvesting apples, an important agricultural product, relies on efficient and accurate detection and localization technology to ensure
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The use of automation technology in agriculture has become particularly important as global agriculture is challenged by labor shortages and efficiency gains. The automated process for harvesting apples, an important agricultural product, relies on efficient and accurate detection and localization technology to ensure the quality and quantity of production. Adverse lighting conditions can significantly reduce the accuracy of fruit detection and localization in automated apple harvesting. Based on deep-learning techniques, this study aims to develop an accurate fruit detection and localization method under adverse light conditions. This paper explores the LE-YOLO model for accurate and robust apple detection and localization. The traditional YOLOv5 network was enhanced by adding an image enhancement module and an attention mechanism. Additionally, the loss function was improved to enhance detection performance. Secondly, the enhanced network was integrated with a binocular camera to achieve precise apple localization even under adverse lighting conditions. This was accomplished by calculating the 3D coordinates of feature points using the binocular localization principle. Finally, detection and localization experiments were conducted on the established dataset of apples under adverse lighting conditions. The experimental results indicate that LE-YOLO achieves higher accuracy in detection and localization compared to other target detection models. This demonstrates that LE-YOLO is more competitive in apple detection and localization under adverse light conditions. Compared to traditional manual and general automated harvesting, our method enables automated work under various adverse light conditions, significantly improving harvesting efficiency, reducing labor costs, and providing a feasible solution for automation in the field of apple harvesting.
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(This article belongs to the Section Digital Agriculture)
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Drone-Based Multispectral Remote Sensing Inversion for Typical Crop Soil Moisture under Dry Farming Conditions
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Tengteng Qu, Yaoyu Li, Qixin Zhao, Yunzhen Yin, Yuzhi Wang, Fuzhong Li and Wuping Zhang
Agriculture 2024, 14(3), 484; https://doi.org/10.3390/agriculture14030484 - 16 Mar 2024
Abstract
Drone multispectral technology enables the real-time monitoring and analysis of soil moisture across vast agricultural lands. overcoming the time-consuming, labor-intensive, and spatial discontinuity constraints of traditional methods. This study establishes a rapid inversion model for deep soil moisture (0–200 cm) in dryland agriculture
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Drone multispectral technology enables the real-time monitoring and analysis of soil moisture across vast agricultural lands. overcoming the time-consuming, labor-intensive, and spatial discontinuity constraints of traditional methods. This study establishes a rapid inversion model for deep soil moisture (0–200 cm) in dryland agriculture using data from drone-based multispectral remote sensing. Maize, millet, sorghum, and potatoes were selected for this study, with multispectral data, canopy leaf, and soil moisture content at various depths collected every 3 to 6 days. Vegetation indices highly correlated with crop canopy leaf moisture content (p < 0.01) and were identified using Pearson correlation analysis, leading to the development of linear and nonlinear regression models for predicting moisture content in canopy leaves and soil. The results show a significant linear correlation between the predicted and actual canopy leaf moisture levels for the four crops, according to the chosen vegetation indices. The use of canopy leaf moisture content to predict surface soil moisture (0–20 cm) demonstrated enhanced accuracy. The models designed for the top 20 cm of soil moisture successfully estimated deep soil moisture levels (up to 200 cm) for all four crops. The 20 cm range soil moisture model showed improvements over the 10 cm range model, with increases in Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Nash–Sutcliffe Efficiency Coefficient (NSE) by 0.4, 0.8, 0.73, and 0.34, respectively, in the corn area; 0.28, 0.69, 0.48, and 0.25 in the millet area; 0.4, 0.48, 0.22, and 0.52 in the sorghum area; and 1.14, 0.81, 0.73, and 0.56 in the potato area, all with an average Relative Error (RE) of less than 10% across the crops. Using drone-based multispectral technology, this study forecasts leaf water content via vegetation index analysis, facilitating swift and effective soil moisture inversion. This research introduces a novel method for monitoring and managing agricultural water resources, providing a scientific basis for precision farming and moisture variation monitoring in dryland areas.
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(This article belongs to the Section Digital Agriculture)
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The Effect of Long-Term Crop Rotations for the Soil Carbon Sequestration Rate Potential and Cereal Yield
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Lina Skinulienė, Aušra Marcinkevičienė, Mindaugas Dorelis and Vaclovas Bogužas
Agriculture 2024, 14(3), 483; https://doi.org/10.3390/agriculture14030483 - 16 Mar 2024
Abstract
Depending on the type of agricultural use and applied crop rotation, soil organic carbon accumulation may depend, which can lead to less CO2 fixation in the global carbon cycle. Less is known about organic carbon emissions in different crop production systems (cereals,
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Depending on the type of agricultural use and applied crop rotation, soil organic carbon accumulation may depend, which can lead to less CO2 fixation in the global carbon cycle. Less is known about organic carbon emissions in different crop production systems (cereals, grasses) using different agrotechnologies. There is a lack of more detailed studies on the influence of carbon content in the soil on plant productivity, as well as the links between the physical properties of the soil and the absorption, viability, and emission of greenhouse gases (GHG) from mineral fertilizers. The aim of this study is to estimate the long-term effect of soil organic carbon sequestration potential in different crop rotations. The greatest potential for organic carbon sequestration is Norfolk-type crop rotation, where crops that reduce soil fertility are replaced by crops that increase soil fertility every year. Soil carbon sequestration potential was significantly higher (46.72%) compared with continuous black fallow and significantly higher from 27.70 to 14.19% compared with field with row crops and cereal crop rotations, respectively, intensive crop rotation saturated with intermediate crops. In terms of carbon sequestration, it is most effective to keep perennial grasses for one year while the soil is still full of undecomposed cereal straw from the previous crop. Black fallow without manure fertilization, compared to crop rotation, reduces the amount of organic carbon in the soil up to two times, the carbon management index by 2–5 times, and poses the greatest risk to the potential of carbon sequestration in agriculture.
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(This article belongs to the Special Issue Greenhouse Gas Emissions in Agricultural System and Green Infrastructures: Mechanisms and Mitigation Measures)
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Seasonal Dynamics of Epigeic Arthropods under the Conditions of Ecological Management of the Triticum aestivum Crop
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Vladimír Langraf and Kornélia Petrovičová
Agriculture 2024, 14(3), 482; https://doi.org/10.3390/agriculture14030482 - 16 Mar 2024
Abstract
The policy of the European Union on land management promotes sustainable agriculture with an emphasis on the protection of biodiversity and the environment. Organic agriculture is the most appropriate alternative to ensure this common goal. The aim of this study was to determine
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The policy of the European Union on land management promotes sustainable agriculture with an emphasis on the protection of biodiversity and the environment. Organic agriculture is the most appropriate alternative to ensure this common goal. The aim of this study was to determine the influence of factors such as pH, moisture, nitrogen potassium, phosphorus and grass herbaceous vegetation on the spatial structure of epigeic arthropods during the spring and summer seasons under organic farming conditions. Research took place between 2020 and 2022, and we recorded 14,988 individuals belonging to 16 taxa using pitfall traps. Between the years 2020 and 2022, we confirmed a decrease in the number of individuals and taxa of epigeic arthropods from the grass herbaceous vegetation to the interior of the field during the summer seasons. This decline was not confirmed in the spring seasons. Phosphorus, potassium, nitrogen, moisture and pH factors also had a significant influence on the spatial structure of epigeic arthropods. Our results show that the higher number of individuals and taxa at the grass herbaceous vegetation occurred only during the summer period. This fact contributes to an increase in biomass and, consequently, the yield of crops.
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(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)
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Open AccessReview
A Review of Machine Learning Techniques in Agroclimatic Studies
by
Dania Tamayo-Vera, Xiuquan Wang and Morteza Mesbah
Agriculture 2024, 14(3), 481; https://doi.org/10.3390/agriculture14030481 - 16 Mar 2024
Abstract
The interplay of machine learning (ML) and deep learning (DL) within the agroclimatic domain is pivotal for addressing the multifaceted challenges posed by climate change on agriculture. This paper embarks on a systematic review to dissect the current utilization of ML and DL
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The interplay of machine learning (ML) and deep learning (DL) within the agroclimatic domain is pivotal for addressing the multifaceted challenges posed by climate change on agriculture. This paper embarks on a systematic review to dissect the current utilization of ML and DL in agricultural research, with a pronounced emphasis on agroclimatic impacts and adaptation strategies. Our investigation reveals a dominant reliance on conventional ML models and uncovers a critical gap in the documentation of methodologies. This constrains the replicability, scalability, and adaptability of these technologies in agroclimatic research. In response to these challenges, we advocate for a strategic pivot toward Automated Machine Learning (AutoML) frameworks. AutoML not only simplifies and standardizes the model development process but also democratizes ML expertise, thereby catalyzing the advancement in agroclimatic research. The incorporation of AutoML stands to significantly enhance research scalability, adaptability, and overall performance, ushering in a new era of innovation in agricultural practices tailored to mitigate and adapt to climate change. This paper underscores the untapped potential of AutoML in revolutionizing agroclimatic research, propelling forward the development of sustainable and efficient agricultural solutions that are responsive to the evolving climate dynamics.
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(This article belongs to the Special Issue Application of Machine Learning and Data Analysis in Agriculture)
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Open AccessArticle
Enhancing Sustainable Agriculture in China: A Meta-Analysis of the Impact of Straw and Manure on Crop Yield and Soil Fertility
by
Zhe Zhao, Yali Yang, Hongtu Xie, Yixin Zhang, Hongbo He, Xudong Zhang and Shijun Sun
Agriculture 2024, 14(3), 480; https://doi.org/10.3390/agriculture14030480 - 16 Mar 2024
Abstract
As the main organic materials, straw and manure play a critical role in soil organic carbon (SOC) sequestration and crop yield in China. This meta-analysis evaluated the impact of straw and manure amendments, both individually and combined, on crop yield, SOC, and soil
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As the main organic materials, straw and manure play a critical role in soil organic carbon (SOC) sequestration and crop yield in China. This meta-analysis evaluated the impact of straw and manure amendments, both individually and combined, on crop yield, SOC, and soil nutrients in China by collecting 173 studies. The findings of this study revealed that straw return and manure application increased crop yields by 14.4% and 70.4%, respectively, overall. Combined straw and manure application gained a better improvement effect than straw alone but was less effective than manure alone. Regarding the straw return results, rice straw and a 3000–6000 kg ha−1 returning quantity improved crop yield, SOC, available phosphorus (AP), available potassium (AK), and total nitrogen (TN) the most; regarding the straw return form, straw incorporated into soil and biochar increased crop yield and SOC more, respectively; and <5 years and ≥5 years of straw return treatment increased crop yield and TN more, respectively. Regarding manure application, pig and chicken manure increased crop yield and TN more, respectively; a 50–80% substitution ratio and 10–20 years of duration were best for improving crop yield, SOC, AP, AK, and TN. This study highlights the importance of optimal organic amendment through straw or manure applications to achieve a win–win between crop yield and soil fertility under the requirement of sustainable agriculture.
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(This article belongs to the Special Issue Soil Management for Sustainable Agriculture)
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Open AccessArticle
The Effect of Grape Seed Cake as a Dietary Supplement Rich in Polyphenols on the Quantity and Quality of Milk, Metabolic Profile of Blood, and Antioxidative Status of Lactating Dairy Goats
by
Zvonko Antunović, Josip Novoselec, Željka Klir Šalavardić, Zvonimir Steiner, Mato Drenjančević, Valentina Pavić, Mislav Đidara, Mario Ronta, Lidija Jakobek Barron and Boro Mioč
Agriculture 2024, 14(3), 479; https://doi.org/10.3390/agriculture14030479 - 15 Mar 2024
Abstract
The objective of this study was to assess the impact that diets supplemented with grape seed cake rich in polyphenols had on lactating goats. The study investigated the quantity and quality of goat milk, the metabolic profile of blood, and the antioxidative status.
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The objective of this study was to assess the impact that diets supplemented with grape seed cake rich in polyphenols had on lactating goats. The study investigated the quantity and quality of goat milk, the metabolic profile of blood, and the antioxidative status. The study involved 24 French Alpine dairy goats throughout their lactation period. The goats were, on average, 5 years old (±three months) and in the fourth lactation. The experiment lasted for 58 days. The control group (CON) had a diet without grape seed cake (GSC). The experimental groups were given a diet containing 5% and 10% GSC on a dry matter basis (GSC5 and GSC10, respectively). A slightly higher milk production, as well as protein and fat milk content, were found in GSC5 and GSC10, but the differences were not significant. Goat milk in the GSC10 group exhibited significantly higher activity of superoxide dismutase and glutathione reductase, as well as decreased concentrations of GUK and SCC. The feeding treatments did not affect significant differences in hematological and biochemical indicators, except for the BHB content, which can be associated with a higher energy value of feed containing GSC. There was an observed elevation in the activity of SOD within the blood of GSC5, and GSC10 was measured as well. The determined changes justify the supplementation of GSC rich in polyphenols to goat feed, especially in the amount of 10%, as it can reduce stress caused by lactation, which is known as a very stressful production period for animals.
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(This article belongs to the Special Issue Rational Use of Feed to Promote Animal Healthy Feeding)
Open AccessArticle
Digital Village Construction: A Multi-Level Governance Approach to Enhance Agroecological Efficiency
by
Jin Ren, Xinrui Chen, Lefeng Shi, Ping Liu and Zhixiong Tan
Agriculture 2024, 14(3), 478; https://doi.org/10.3390/agriculture14030478 - 15 Mar 2024
Abstract
This study conducts a comprehensive analysis of China’s digital village construction, emphasizing its role in rural organizational governance, from bureaucracies to self-governance bodies to market forces and social organizations. Utilizing sample data from 30 provinces from 2014 to 2020, the study dissects the
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This study conducts a comprehensive analysis of China’s digital village construction, emphasizing its role in rural organizational governance, from bureaucracies to self-governance bodies to market forces and social organizations. Utilizing sample data from 30 provinces from 2014 to 2020, the study dissects the dynamics and diversity of multi-level governance in bolstering agroecological efficiency (AEE). Notable insights include a significant positive correlation between digital villages and AEE. However, it wanes in an “inverted U” pattern beyond a digital development index of 0.8. Furthermore, rural bureaucrats and self-governing entities independently advance AEE, while market forces and social organizations require enhancement. These findings contribute to the field of digital village construction and inform sustainable agricultural strategies in developing nations.
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(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Evaluating the Path to the European Commission’s Organic Agriculture Goal: A Multivariate Analysis of Changes in EU Countries (2004–2021) and Socio-Economic Relationships
by
Stefan Krajewski, Jan Žukovskis, Dariusz Gozdowski, Marek Cieśliński and Elżbieta Wójcik-Gront
Agriculture 2024, 14(3), 477; https://doi.org/10.3390/agriculture14030477 - 15 Mar 2024
Abstract
This study comprehensively analyzed the dynamic landscape of organic farming in the European Union (EU) from 2004 to 2021, investigating the shifts in dedicated agricultural areas influenced by evolving preferences and the priorities of farmers and consumers. Examining the impact of socio-economic factors,
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This study comprehensively analyzed the dynamic landscape of organic farming in the European Union (EU) from 2004 to 2021, investigating the shifts in dedicated agricultural areas influenced by evolving preferences and the priorities of farmers and consumers. Examining the impact of socio-economic factors, including gross domestic product (GDP) per capita, the human development index (HDI), and human population density, this study established multivariate relationships through country-level analyses based on correlations, principal component analysis, cluster analysis, and panel analysis. Despite a universal increase in the organic agriculture areas across all the EU countries during the study period, the production levels exhibited negative correlations with the human population density, GDP per capita, and HDI. Notably, the Baltic countries and Austria led in organic farming production, while Malta, the Netherlands, Belgium, Ireland, and Luxemburg formed a distinct group in the cluster analysis with less intensive organic agriculture per capita. These insights are crucial for supporting the resilience and sustainability of organic farming as it continues to evolve. Predictions of organic agriculture share for 2030 based on trends evaluated using linear regression in the years 2004–2021 estimated about 12% of utilized agricultural area, which was much lower than the target share of the European Commission at 25%. Predictions based on linear regression showed that achieving the European Green Deal target of a 25% share of organic agriculture in unlikely in most EU countries by 2030. The target is only highly probable to be obtained in Austria, Estonia, and Sweden. The EU countries varied significantly across various indices characterizing organic agriculture, including organic agriculture area share. It should be noted that the study was conducted on the data obtained prior to the outbreak of the war in Ukraine, which could potentially alter the previous trends in the development of organic agriculture in the EU.
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(This article belongs to the Special Issue Advances in Organic Agriculture—Decreasing Yield Gap via Optimising Cultivation Methods and Agrarian Policy)
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