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Agriculture, Volume 14, Issue 3 (March 2024) – 179 articles

Cover Story (view full-size image): Plasmopara viticola is one of the most destructive pathogens affecting grape production today. However, traditional chemical management practices have begun to fall out of favor due to increased fungicide resistance and environmental concerns. A better understanding of the pathogen, its genetics, and integrated disease management programs that optimize all available treatment options will help reduce its negative impacts on growers. From targeted breeding to biological control agents, potential solutions are continually being investigated and adapted to limit the damage caused by downy mildew. This review summarizes the current knowledge of the pathogen and the methods of its control and explores potential avenues for future research focused on hypovirulence and biological control agents. View this paper
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11 pages, 1252 KiB  
Article
Decomposition of Hemp Residues in Soil as Facilitated by Different Nitrogen Sources
by Urte Stulpinaite, Vita Tilvikiene and Modupe Olufemi Doyeni
Agriculture 2024, 14(3), 508; https://doi.org/10.3390/agriculture14030508 - 21 Mar 2024
Viewed by 797
Abstract
Improving soil health across agroecosystems has continued to receive attention around the globe, with an emphasis on sustainable organic inputs from agricultural practice. It is well known that different organic materials, such as composts, manure and cereal straws, positively affect soil carbon. The [...] Read more.
Improving soil health across agroecosystems has continued to receive attention around the globe, with an emphasis on sustainable organic inputs from agricultural practice. It is well known that different organic materials, such as composts, manure and cereal straws, positively affect soil carbon. The changing agricultural practices have continuously led to new and improved plants in farming. One of these innovative plants is industrial hemp. With the increasing cultivation of industrial hemp globally, the problem of the disposal of hemp residues has been encountered. However, the rich carbon content found in hemp residues in soil is anticipated to enhance the soil quality and address the challenge of effectively utilizing hemp straw. In this study, we conducted a two-way experimental trial to evaluate the decomposition of hemp residues using placement methods (residues incorporated into the soil or left on the soil surface) and nitrogen sources as additives. Different nitrogen additives (nitrogen fertilizer pellets, liquid nitrogen, organic fertilizers, and the preparation “Bioversio”) were selected to accelerate the decomposition of hemp residues. The results showed that the mineralization rates were faster in the residues incorporated in the soil, with a mass loss of over 54% compared to the treatments left on the soil. The influence of additives on the decomposition rates was statistically significant. Additionally, there was a significant increase in the N content in the soil, while the change in carbon content in the soil was not statistically significant. These research results reinforce nitrogen fertilizers’ positive role in accelerating hemp residue decomposition in soil. Furthermore, our findings will help contribute to the effective and sustainable utilization of hemp residues as a bioresource material to improve soil health. Full article
(This article belongs to the Section Agricultural Soils)
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21 pages, 6580 KiB  
Article
Automated Grading of Angelica sinensis Using Computer Vision and Machine Learning Techniques
by Zimei Zhang, Jianwei Xiao, Wenjie Wang, Magdalena Zielinska, Shanyu Wang, Ziliang Liu and Zhian Zheng
Agriculture 2024, 14(3), 507; https://doi.org/10.3390/agriculture14030507 - 21 Mar 2024
Viewed by 738
Abstract
Angelica sinensis (Oliv.) Diels, a member of the Umbelliferae family, is commonly known as Danggui (Angelica sinensis, AS). AS has the functions of blood tonic, menstrual pain relief, and laxatives. Accurate classification of AS grades is crucial for [...] Read more.
Angelica sinensis (Oliv.) Diels, a member of the Umbelliferae family, is commonly known as Danggui (Angelica sinensis, AS). AS has the functions of blood tonic, menstrual pain relief, and laxatives. Accurate classification of AS grades is crucial for efficient market management and consumer health. The commonly used method to classify AS grades depends on the evaluator’s observation and experience. However, this method has issues such as unquantifiable parameters and inconsistent identification results among different evaluators, resulting in a relatively chaotic classification of AS in the market. To address these issues, this study introduced a computer vision-based approach to intelligently grade AS. Images of AS at five grades were acquired, denoised, and segmented, followed by extraction of shape, color, and texture features. Thirteen feature parameters were selected based on difference and correlation analysis, including tail area, whole body area, head diameter, G average, B average, R variances, G variances, B variances, R skewness, G skewness, B skewness, S average, and V average, which exhibited significant differences and correlated with grades. These parameters were then used to train and test both the traditional back propagation neural network (BPNN) and the BPNN model improved with a growing optimizer (GOBPNN). Results showed that the GOBPNN model achieved significantly higher average testing precision, recall, F-score, and accuracy (97.1%, 95.9%, 96.5%, and 95.0%, respectively) compared to the BPNN model. The method combining machine vision technology with GOBPNN enabled efficient, objective, rapid, non-destructive, and cost effective AS grading. Full article
(This article belongs to the Special Issue Agricultural Products Processing and Quality Detection)
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14 pages, 7274 KiB  
Article
The Mechanical Analysis and Comparative Performance Test of the Roller-Type Pulling Mechanism for the Whole Cotton Stalk Pulling Machine
by Yichao Wang, Jiaxi Zhang, Shilong Shen, Jinming Li, Yanjun Huo and Zhenwei Wang
Agriculture 2024, 14(3), 506; https://doi.org/10.3390/agriculture14030506 - 21 Mar 2024
Viewed by 561
Abstract
In order to address the common difficulties in pulling and harvesting whole cotton stalks, such as high pulling resistance, high miss-pulling rate, and high breakage rate, which severely hinder the recycling of cotton stalks, three different pulling mechanisms with different pulling principles (wrapping-type [...] Read more.
In order to address the common difficulties in pulling and harvesting whole cotton stalks, such as high pulling resistance, high miss-pulling rate, and high breakage rate, which severely hinder the recycling of cotton stalks, three different pulling mechanisms with different pulling principles (wrapping-type pulling mechanism, clamping-type pulling mechanism, transverse roller-type pulling mechanism) were designed. The pulling force on cotton stalks during the pulling process of the three different roller-type pulling mechanisms was compared and analyzed, clarifying the mechanism of roller-type whole cotton stalk pulling mechanisms and identifying situations with optimal pulling force. Field comparative experiments were conducted to compare the working performance of different roller-type pulling mechanisms in the field, and a comprehensive analysis of two key indicators in pulling cotton stalks, miss-pulling rate and breakage rate, was carried out. The results showed that the pulling method and pulling force of the pulling mechanism played a crucial role in the successful pulling of cotton straws. Comparative analysis of the three pulling mechanisms revealed that the clamping-type pulling mechanism had the highest pulling force. The standard deviation means of the missed pull rates for mechanisms X1, X2, and X3 were 0.83%, 0.59%, and 0.43%, respectively, while the standard deviation means of the breakage rates were 1.48%, 1.79%, and 0.49%, respectively. The enveloping-type pulling mechanism had a higher missed pull rate with an average of 8.32%, and the clamping-type pulling mechanism resulted in excessive breakage of cotton straw during operation, with an average breakage rate of 14.10%. In contrast, the transverse roller-type straw pulling mechanism performed the best in the field performance test, as it did not require precise alignment and had an average missed pull rate of 4.55% and an average breakage rate of only 7.55%. Considering the practical needs of agriculture production, the transverse roller-type straw pulling mechanism is recommended as the pulling device for cotton straw harvesting. The research results can provide a reference for the design selection of whole-plant straw pulling mechanisms. Full article
(This article belongs to the Section Agricultural Technology)
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17 pages, 656 KiB  
Article
Influence of Foliar Application of Microelements on Yield and Yield Components of Spring Malting Barley
by Barbara Stadnik, Renata Tobiasz-Salach and Dagmara Migut
Agriculture 2024, 14(3), 505; https://doi.org/10.3390/agriculture14030505 - 21 Mar 2024
Viewed by 668
Abstract
Barley is an economically important plant cultivated primarily for animal feed and in the brewing industry for the production of barley malt. Climate changes and an increase in grain demand result in a constant need to improve the volume and stability of cereal [...] Read more.
Barley is an economically important plant cultivated primarily for animal feed and in the brewing industry for the production of barley malt. Climate changes and an increase in grain demand result in a constant need to improve the volume and stability of cereal species yields and better use the potential of cultivars. In cereal production, an important aspect is the use of microelements, especially by foliar spraying. Microelements, as components or enzyme activators, play a significant role in plant growth and metabolic processes occurring in the cell. As a consequence, their availability is a factor determining plant development. The aim of this study was to determine the effect of foliar fertilization with selected microelements on the yield of two-row malting barley cultivars. In 2019–2021, a two-factor field experiment with barley was conducted in south-eastern Poland. The experimental factors were three spring barley cultivars (Baryłka, KWS Irina, and RGT Planet) of the brewing type and four single-component micronutrient fertilizers containing copper (Cu), manganese (Mn), molybdenum (Mo), and zinc (Zn). The foliar application of microelements resulted in improvements in selected elements of the yield structure and an increase in grain yield, and the effect depended on the fertilization applied. The highest grain yield was obtained from plots where fertilizer with Mo or Zn was used. Barley plants sprayed with Mo fertilizer developed the longest spikes and were characterized by the highest number of productive tillers per plant. The foliar application of Zn resulted in the formation of the highest number of spikes per unit area and grain uniformity. The RGT Planet cultivar was characterized by higher values of the measured parameters compared to Baryłka and KWS Irina. Full article
(This article belongs to the Special Issue Foliar Fertilization for Sustainable Crop Production)
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8 pages, 170 KiB  
Editorial
Economic Strategies and Policy Suggestions of Agricultural Sustainable Food Production
by Filiberto Altobelli and Roberto Henke
Agriculture 2024, 14(3), 504; https://doi.org/10.3390/agriculture14030504 - 20 Mar 2024
Viewed by 661
Abstract
Sustainability is increasingly becoming a keyword for viable agriculture and food production [...] Full article
15 pages, 18883 KiB  
Article
Meteorological and Agricultural Drought Risk Assessment via Kaplan–Meier Survivability Estimator
by Cem Polat Cetinkaya and Mert Can Gunacti
Agriculture 2024, 14(3), 503; https://doi.org/10.3390/agriculture14030503 - 20 Mar 2024
Viewed by 669
Abstract
Dry periods and drought are inherent natural occurrences. However, due to the increasing pressures of global warming and climate change, these events have become more frequent and severe on a global scale. These phenomena can be traced with various indicators and related indices [...] Read more.
Dry periods and drought are inherent natural occurrences. However, due to the increasing pressures of global warming and climate change, these events have become more frequent and severe on a global scale. These phenomena can be traced with various indicators and related indices proposed by various scholars. In general, drought risk assessment is done by modeling these indicators and determining the drought occurrence probabilities. The proposed adaptation introduces the “Kaplan–Meier estimator”, a non-parametric statistic traditionally used in medical contexts to estimate survival functions from lifetime data. The study aims to apply this methodology to assess drought risk by treating past droughts as “events” and using drought indicators such as the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). Mapping these results for a better understanding of the drought risks on larger spatial scales such as a river basin is also within the expected outcomes. The adapted method provides the probability of non-occurrence, with inverted results indicating the likelihood of drought occurrence. As a case study, the method is applied to SPI and SPEI values at different time steps (3, 6, and 12 months) across 27 meteorological stations in the Gediz River Basin, located in Western Turkey—a region anticipated to be profoundly affected by global climate change. The results are represented as the generated drought risk maps and curves, which indicate that (i) drought risks increase as the considered period extends, (ii) drought risks decrease as the utilized indicator timescales increase, (iii) locally plotted drought curves indicate higher drought risks as their initial slope gets steeper. The method used enables the generation of historical evidence based spatially distributed drought risk maps, which expose more vulnerable areas within the river basin. Full article
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35 pages, 3137 KiB  
Article
An Assessment of Food Value Chains to Identify Gaps and Make Recommendations for Further Development: A Slovenian Case Study
by Jernej Prišenk, Jernej Turk, Karmen Pažek, Črtomir Rozman, Andreja Borec and Nejc Zidar
Agriculture 2024, 14(3), 502; https://doi.org/10.3390/agriculture14030502 - 20 Mar 2024
Viewed by 894
Abstract
The content of this paper presents the research results of a three-year research project in which a multi-criteria evaluation model (according to the DEX methodology) was developed for the evaluation of three different food sectors (represented by a cattle breeding chain, a pig [...] Read more.
The content of this paper presents the research results of a three-year research project in which a multi-criteria evaluation model (according to the DEX methodology) was developed for the evaluation of three different food sectors (represented by a cattle breeding chain, a pig farming chain, and a milk production chain) with added value in Slovenia. Indicators for the assessment of the economically, socially, and environmentally sustainable development of food chains were taken into account. The data for the analysis, such as prices and costs of food, wage levels by sector, food miles and others, were obtained from various public services between 2020 and 2023. The final qualitative assessment of the food sectors was uniform (“average”), while the longest analysis of the results using the plus-minus-1 analysis method showed the reasons for such an assessment in individual sectors (such as the ratio between the price of agricultural products and the price of agricultural inputs is poor, the ratio between average gross salary in the individual food sector and gross salary in the agricultural sector is poor, etc.). In addition to the results already mentioned, recommendations or suggestions for building a sustainable food chain were made using the results of the modelling. The research results contributed to a better understanding of the importance of stable relationships between different groups of indicators and later showed their importance for improving the functioning of agri-food chains. The results of the research will help various stakeholders (such as the agricultural advisory service, decision-makers at the level of agricultural policy, researchers in further analyses, and especially the international professional public interested in various case studies from EU countries) to further analyse and plan for the organisation of the agricultural sector. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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19 pages, 4139 KiB  
Article
Potential Risk of Frost in the Growing Season in Poland
by Jadwiga Nidzgorska-Lencewicz, Agnieszka Mąkosza, Czesław Koźmiński and Bożena Michalska
Agriculture 2024, 14(3), 501; https://doi.org/10.3390/agriculture14030501 - 20 Mar 2024
Viewed by 609
Abstract
Fruits, garden plants, and agricultural crops grown in Poland exhibit wide variations in their sensitivity to frost, particularly in early spring. In the case of frost, generally, the yield and quality are reduced, and sometimes, entire plants can be destroyed. This article characterizes [...] Read more.
Fruits, garden plants, and agricultural crops grown in Poland exhibit wide variations in their sensitivity to frost, particularly in early spring. In the case of frost, generally, the yield and quality are reduced, and sometimes, entire plants can be destroyed. This article characterizes the occurrence of ground frosts (at 5 cm agl) and air frosts (at 200 cm agl) in Poland gathered from 52 meteorological stations affiliated with IMGW-PIB between 1971 and 2020. To assess the real risk of frost to plants, the variability of this phenomenon was analyzed per thermal growing season (defined as air temperature >5 °C), rather than in traditional calendar terms as presented in most studies. In the climatic conditions of Poland, the growing season is characterized by a reported 28 days with ground frost and 13.3 days with air frost, approximately. In spring, the last ground frost disappears, on average, on a country scale, on May 14, and air frost on April 27. In turn, in autumn, the first ground frost is recorded, on average, on 1 October and air frost on 14 October. On the basis of the selected characteristics of frost and the growing season, four areas of potential risk of ground and air frost in the growing season, as well as in spring, were determined with the use of cluster analysis. Full article
(This article belongs to the Section Crop Production)
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22 pages, 5073 KiB  
Article
Combinations of Feature Selection and Machine Learning Models for Object-Oriented “Staple-Crop-Shifting” Monitoring Based on Gaofen-6 Imagery
by Yujuan Cao, Jianguo Dai, Guoshun Zhang, Minghui Xia and Zhitan Jiang
Agriculture 2024, 14(3), 500; https://doi.org/10.3390/agriculture14030500 - 20 Mar 2024
Viewed by 663
Abstract
This paper combines feature selection with machine learning algorithms to achieve object-oriented classification of crops in Gaofen-6 remote sensing images. The study provides technical support and methodological references for research on regional monitoring of food crops and precision agriculture management. “Staple-food-shifting” refers to [...] Read more.
This paper combines feature selection with machine learning algorithms to achieve object-oriented classification of crops in Gaofen-6 remote sensing images. The study provides technical support and methodological references for research on regional monitoring of food crops and precision agriculture management. “Staple-food-shifting” refers to the planting of other cash crops on cultivated land that should have been planted with staple crops such as wheat, rice, and maize, resulting in a change in the type of arable land cultivated. An accurate grasp of the spatial and temporal patterns of “staple-food-shifting” on arable land is an important basis for rationalizing land use and protecting food security. In this study, the Shihezi Reclamation Area in Xinjiang is selected as the study area, and Gaofen-6 satellite images are used to study the changes in the cultivated area of staple food crops and their regional distribution. Firstly, the images are segmented at multiple scales and four types of features are extracted, totaling sixty-five feature variables. Secondly, six feature selection algorithms are used to optimize the feature variables, and a total of nine feature combinations are designed. Finally, k-Nearest Neighbor (KNN), Random Forest (RF), and Decision Tree (DT) are used as the basic models of image classification to explore the best combination of feature selection method and machine learning model suitable for wheat, maize, and cotton classification. The results show that our proposed optimal feature selection method (OFSM) can significantly improve the classification accuracy by up to 15.02% compared to the Random Forest Feature Importance Selection (RF-FI), Random Forest Recursive Feature Elimination (RF-RFE), and XGBoost Feature Importance Selection (XGBoost-FI) methods. Among them, the OF-RF-RFE model constructed based on KNN performs the best, with the overall accuracy, average user accuracy, average producer accuracy, and kappa coefficient reaching 90.68%, 87.86%, 86.68%, and 0.84, respectively. Full article
(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring)
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16 pages, 1974 KiB  
Article
Model Development for Off-Road Traction Control: A Linear Parameter-Varying Approach
by Adam Szabo, Daniel Karoly Doba, Szilard Aradi and Peter Kiss
Agriculture 2024, 14(3), 499; https://doi.org/10.3390/agriculture14030499 - 19 Mar 2024
Viewed by 651
Abstract
The number of highly automated machines in the agricultural sector has increased rapidly in recent years. To reduce their fuel consumption, and thus their emission and operational cost, the performance of such machines must be optimized. The running gear–terrain interaction heavily affects the [...] Read more.
The number of highly automated machines in the agricultural sector has increased rapidly in recent years. To reduce their fuel consumption, and thus their emission and operational cost, the performance of such machines must be optimized. The running gear–terrain interaction heavily affects the behavior of the vehicle; therefore, off-road traction control algorithms must effectively handle this nonlinear phenomenon. This paper proposes a linear parameter-varying model that retains the generality of semiempirical models while supporting the development of real-time state observers and control algorithms. First, the model is derived from the Bekker–Wong model for the theoretical case of a single wheel; then, it is generalized to describe the behavior of vehicles with an arbitrary number of wheels. The proposed model is validated using an open-source multiphysics simulation engine and experimental measurements. According to the validated results, it performs satisfactorily overall in terms of model complexity, calculation cost, and accuracy, confirming its applicability. Full article
(This article belongs to the Special Issue Design, Optimization and Analysis of Agricultural Machinery)
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14 pages, 1300 KiB  
Article
Eliminating the Pathogen Xanthomonas hortorum pv. carotae from Carrot Seeds Using Different Types of Nanoparticles
by Jan Wohlmuth, Dorota Tekielska, Eliška Hakalová, Jana Čechová, Zuzana Bytešníková, Lukáš Richtera and Miroslav Baránek
Agriculture 2024, 14(3), 498; https://doi.org/10.3390/agriculture14030498 - 19 Mar 2024
Viewed by 686
Abstract
There exists a wide range of plant pathogens that are commonly referred to as seed-borne pathogens due to their dominant mode of spread. Treating seeds to eliminate such pathogens is therefore very important in contemporary seed production. In the present study, eight types [...] Read more.
There exists a wide range of plant pathogens that are commonly referred to as seed-borne pathogens due to their dominant mode of spread. Treating seeds to eliminate such pathogens is therefore very important in contemporary seed production. In the present study, eight types of nanoparticles were evaluated for their effectiveness against Xanthomonas hortorum pv. carotae, a seed-borne pathogen that affects plants of the Apiaceae family. Initially, parameters considering the inhibitory and bactericidal activity of individual nanoparticles were evaluated under in vitro conditions. In this way, three nanoparticles based on copper, silver, and silver/selenium composite were identified as being the most effective. Subsequently, their ability to eliminate Xanthomonas hortorum pv. carotae from artificially infected carrot seeds was tested. This was achieved through the qPCR quantification of the pathogen in 14-day-old plantlets developed from seeds inoculated with Xhc. Based on the obtained results, copper-based nanoparticles were the most effective, resulting in an approximately 10-fold decrease in the occurrence of Xhc in plantlets compared to the untreated control. Taking into account the fact that X. hortorum pathovars also attack other important horticultural crops, the presented results may have a much wider scope than just carrot seeds. Full article
(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)
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13 pages, 16827 KiB  
Article
Potential of Limestonevirus Bacteriophages for Ecological Control of Dickeya solani Causing Bacterial Potato Blackleg
by Martin Kmoch, Josef Vacek, Věra Loubová, Karel Petrzik, Sára Brázdová and Rudolf Ševčík
Agriculture 2024, 14(3), 497; https://doi.org/10.3390/agriculture14030497 - 19 Mar 2024
Viewed by 667
Abstract
Pectinolytic bacteria of the family Enterobacteriaceae, specifically Dickeya solani, are known to cause potato blackleg. This study aimed to evaluate the effectiveness of a mixture of two bacteriophages from the genus Limestonevirus in controlling Dickeya solani in both greenhouse and field [...] Read more.
Pectinolytic bacteria of the family Enterobacteriaceae, specifically Dickeya solani, are known to cause potato blackleg. This study aimed to evaluate the effectiveness of a mixture of two bacteriophages from the genus Limestonevirus in controlling Dickeya solani in both greenhouse and field trials. The potential of bacteriophages for ecological potato control was also assessed. The phages φDs3CZ and φDs20CZ were isolated from soil in the Czech Republic between 2019 and 2021. They were applied preventively and curatively as a solution on artificially wounded and inoculated seed tubers immediately prior to planting. The phage-treated variant showed a highly significant reduction in the extent of D. solani infection compared to the untreated control in both the greenhouse and field trial. The effect of the phages depended on the concentration of the solution, the rate of tuber injury, and the sequence of application. When applied preventively, the phages caused a significantly higher reduction in the rate of blackleg symptoms (86.7% and/or 87.1%) compared to the curative application (54.6 and/or 36.6%). Phages φDs3CZ and φDs20CZ showed potential for use in biological potato control against Dickeya solani. Full article
(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)
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23 pages, 21418 KiB  
Article
Assessing Methane Emissions from Rice Fields in Large Irrigation Projects Using Satellite-Derived Land Surface Temperature and Agronomic Flooding: A Spatial Analysis
by Sellaperumal Pazhanivelan, N. S. Sudarmanian, Vellingiri Geethalakshmi, Murugesan Deiveegan, Kaliaperumal Ragunath, A. P. Sivamurugan and P. Shanmugapriya
Agriculture 2024, 14(3), 496; https://doi.org/10.3390/agriculture14030496 - 19 Mar 2024
Viewed by 945
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 [...] Read more.
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. Full article
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14 pages, 3406 KiB  
Article
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
Viewed by 803
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), [...] Read more.
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. Full article
(This article belongs to the Topic Mediterranean Biodiversity)
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19 pages, 6560 KiB  
Article
Study on the Hole-Forming Performance and Opening of Mulching Film for a Dibble-Type Transplanting Device
by 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
Viewed by 599
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Design, Optimization and Analysis of Agricultural Machinery)
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12 pages, 915 KiB  
Article
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
Viewed by 724
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 [...] Read more.
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)
15 pages, 11856 KiB  
Article
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
Viewed by 597
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 [...] Read more.
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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12 pages, 1954 KiB  
Article
Yield Gap Analysis of Super High-Yielding Rice (>15 t ha−1) in Two Ecological Regions
by Zhongwei Wei, Yuzhu Zhang and Wenyu Jin
Agriculture 2024, 14(3), 491; https://doi.org/10.3390/agriculture14030491 - 18 Mar 2024
Viewed by 619
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 [...] Read more.
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
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27 pages, 5378 KiB  
Article
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
Viewed by 633
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 [...] Read more.
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. Full article
(This article belongs to the Section Digital Agriculture)
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21 pages, 22046 KiB  
Article
An Integrated IoT Sensor-Camera System toward Leveraging Edge Computing for Smart Greenhouse Mushroom Cultivation
by 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
Viewed by 842
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 [...] Read more.
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
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17 pages, 334 KiB  
Review
Progress in Research and Prospects for Application of Precision Gene-Editing Technology Based on CRISPR–Cas9 in the Genetic Improvement of Sheep and Goats
by 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
Viewed by 1014
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 [...] Read more.
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
26 pages, 8276 KiB  
Article
Design and Experiment of Automatic Transport System for Planting Plate in Plant Factory
by 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
Viewed by 805
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 [...] Read more.
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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18 pages, 1647 KiB  
Article
Analysis of the Physico-Chemical Properties of Bean Seeds after Three Years of Digestate Use
by 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
Viewed by 791
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Efficient Use of Irrigation and Fertilizer to Increase Crop Yield)
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15 pages, 4597 KiB  
Article
An Apple Detection and Localization Method for Automated Harvesting under Adverse Light Conditions
by Guoyu Zhang, Ye Tian, Wenhan Yin and Change Zheng
Agriculture 2024, 14(3), 485; https://doi.org/10.3390/agriculture14030485 - 16 Mar 2024
Viewed by 882
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 [...] Read more.
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. Full article
(This article belongs to the Section Digital Agriculture)
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17 pages, 31676 KiB  
Article
Drone-Based Multispectral Remote Sensing Inversion for Typical Crop Soil Moisture under Dry Farming Conditions
by 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
Viewed by 789
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 [...] Read more.
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. Full article
(This article belongs to the Section Digital Agriculture)
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16 pages, 3918 KiB  
Article
The Effect of Long-Term Crop Rotations for the Soil Carbon Sequestration Rate Potential and Cereal Yield
by Lina Skinulienė, Aušra Marcinkevičienė, Mindaugas Dorelis and Vaclovas Bogužas
Agriculture 2024, 14(3), 483; https://doi.org/10.3390/agriculture14030483 - 16 Mar 2024
Viewed by 671
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, [...] Read more.
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. Full article
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14 pages, 5303 KiB  
Article
Seasonal Dynamics of Epigeic Arthropods under the Conditions of Ecological Management of the Triticum aestivum Crop
by Vladimír Langraf and Kornélia Petrovičová
Agriculture 2024, 14(3), 482; https://doi.org/10.3390/agriculture14030482 - 16 Mar 2024
Viewed by 552
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 [...] Read more.
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. Full article
(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)
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19 pages, 1027 KiB  
Review
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
Viewed by 798
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Application of Machine Learning and Data Analysis in Agriculture)
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27 pages, 2913 KiB  
Article
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
Viewed by 641
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Soil Management for Sustainable Agriculture)
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14 pages, 285 KiB  
Article
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
Viewed by 749
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. [...] Read more.
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. Full article
(This article belongs to the Special Issue Rational Use of Feed to Promote Animal Healthy Feeding)
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