Agriculture

24 pages, 6336 KiB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 16 | Viewed by 3330

Abstract

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

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

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

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16 pages, 1042 KiB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 17 | Viewed by 3415

Abstract

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

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

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

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21 pages, 1247 KiB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 26 | Viewed by 5689

Abstract

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

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

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

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19 pages, 4270 KiB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 22 | Viewed by 3053

Abstract

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

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

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14 pages, 3018 KiB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 19 | Viewed by 2676

Abstract

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

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

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

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15 pages, 5880 KiB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 17 | Viewed by 3804

Abstract

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

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

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

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13 pages, 20799 KiB

Open AccessEditor’s ChoiceArticle

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

by Peng Wang, Tong Niu and Dongjian He

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

Cited by 23 | Viewed by 2892

Abstract

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

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

(This article belongs to the Section Agricultural Technology)

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15 pages, 7031 KiB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 32 | Viewed by 4733

Abstract

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

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

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

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20 pages, 2438 KiB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 20 | Viewed by 3235

Abstract

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

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

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

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21 pages, 5250 KiB

Open AccessEditor’s ChoiceArticle

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

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

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

Cited by 21 | Viewed by 7864

Abstract

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

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

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

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15 pages, 312 KiB

Open AccessEditor’s ChoiceArticle

Consumers’ Attitude towards Sustainable Food Consumption during the COVID-19 Pandemic in Romania

by Iulia C. Muresan, Rezhen Harun, Felix H. Arion, Anca Monica Brata, Ioan Aurel Chereches, Gabriela O. Chiciudean, Diana E. Dumitras, Camelia F. Oroian and Olivia Paula Tirpe

Agriculture 2021, 11(11), 1050; https://doi.org/10.3390/agriculture11111050 - 26 Oct 2021

Cited by 28 | Viewed by 6021

Abstract

The COVID-19 pandemic affected consumers’ behavior worldwide. This paper aims to analyze consumers’ sustainable food behavior during the COVID-19 pandemic. The research was based on an online survey during May–October 2020 among 859 Romanian consumers. Principal component analysis and cluster analysis were employed [...] Read more.

The COVID-19 pandemic affected consumers’ behavior worldwide. This paper aims to analyze consumers’ sustainable food behavior during the COVID-19 pandemic. The research was based on an online survey during May–October 2020 among 859 Romanian consumers. Principal component analysis and cluster analysis were employed to group the consumers based on their behavior. Furthermore, the binary-logistic regression was used to identify the socio-demographic profile of the identified groups. Based on the cluster analysis, the consumers were grouped into three main groups: indifferent, pro-environment protection, and health concerned. The results indicate a positive attitude towards sustainable food behavior. Consumers’ attitude towards sustainable food choice is mainly influenced by age and education level. The study offers valuable information for future public policy and marketing campaign regarding sustainable food behavior. Full article

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

16 pages, 522 KiB

Open AccessEditor’s ChoiceArticle

Effects of a One-Time Application of Controlled-Release Nitrogen Fertilizer on Yield and Nitrogen Accumulation and Utilization of Late Japonica Rice in China

by Dong Xu, Ying Zhu, Haibin Zhu, Qun Hu, Guodong Liu, Haiyan Wei and Hongcheng Zhang

Agriculture 2021, 11(11), 1041; https://doi.org/10.3390/agriculture11111041 - 23 Oct 2021

Cited by 15 | Viewed by 2242

Abstract

A mixture of controlled-release nitrogen (N) fertilizers (CRNFs) and conventional urea (CU) as a single application of basal fertilizer could simplify fertilization in rice cultivation from the traditional and more labor-intensive fertilization strategy of multiple applications of nitrogen. However, the reported benefits of [...] Read more.

A mixture of controlled-release nitrogen (N) fertilizers (CRNFs) and conventional urea (CU) as a single application of basal fertilizer could simplify fertilization in rice cultivation from the traditional and more labor-intensive fertilization strategy of multiple applications of nitrogen. However, the reported benefits of this combined approach in increasing rice yield have varied substantially for various reasons, including that various types of rice are characterized by different N requirements to obtain high yield. In this study, two late japonica rice cultivars, Jia58 (J58) and Jia67(J67), were used to determine the best combination of one of two short-acting CRNFs (release periods were 40 and 60 days) and one of three long-acting CRNFs (release periods were 80, 100 and 120 days) to apply with the CU as a one-time application of basal fertilizer. Six combinations of CRNFs were established based on their release periods: A1, 40 + 80 days; A2, 40 + 100 days; A3, 40 + 120 days; B1, 60 + 80 days; B2, 60 + 100 days; and B3, 60 + 120 days. CU applied split at basal, tillering and panicle differentiation stages, respectively as control (CK). The effects of the different treatment combinations of CRNFs on late-rice grain yield, N accumulation and N-use efficiency in a two-year field experiment were determined. Results showed that, the A2 treatment achieved the same yield as that of CK, and yield of the B2 treatment exceeded the yield of CK. Yield of J58 applied with B2 was 7.35% higher in 2018 and 7.40% higher in 2019 than that of the corresponding yield of CK; yield of J67 applied with B2 was 6.05% higher in 2018 and 6.87% higher in 2019 than that of CK. Compared with other CRNF treatments, the release of N from A2 and B2 was most synchronized with nitrogen uptake by the two cultivars, which indicates that fertilizer combination completely met the nitrogen demands during each growth stage of rice. Rice of the A2 and B2 treatments had higher N accumulation, higher aboveground biomass accumulation and LAI (leaf area index) at the heading and maturity stages and higher photosynthetic activity than those of other CRNF treatments. In conclusion, for late japonica rice in China, the application of the A2 and B2 treatments as optimal type of CRNF can achieve labor saving and yield increasing simultaneously in rice production. Full article

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16 pages, 5539 KiB

Open AccessEditor’s ChoiceArticle

Effect of Plant-Growth-Promoting Fungi on Eggplant (Solanum melongena L.) in New Reclamation Land

by Xuqing Li, Dingyi Li, Jianli Yan, Ya Zhang, Hong Wang, Jingze Zhang, Temoor Ahmed and Bin Li

Agriculture 2021, 11(11), 1036; https://doi.org/10.3390/agriculture11111036 - 22 Oct 2021

Cited by 16 | Viewed by 3578

Abstract

Land reclamation may expand the supply of usable land for food security. Immature soil is not suitable for plant growth and needs to be amended by the addition of organic matter and plant growth-promoting (PGP) microorganisms. However, the effects of different PGP fungi [...] Read more.

Land reclamation may expand the supply of usable land for food security. Immature soil is not suitable for plant growth and needs to be amended by the addition of organic matter and plant growth-promoting (PGP) microorganisms. However, the effects of different PGP fungi on plant growth in immature soil are largely unexplored. In order to obtain beneficial soil microorganisms with a good PGP ability in new reclamation land, 162 fungal isolates were isolated from different abandoned wastelands, four isolates of which were obtained in this study by the screening of P solubilization, siderophore production, and indole acetic acid (IAA) production. The result of this study revealed that isolate HZ123 had the highest ability to solubilize P and produce siderophores and IAA, followed by HZ23, HZ10, and HZ06. Based on the results of morphological and molecular analyses, isolate HZ06 was identified as Penicillium oxalicum, isolates HZ23 and HZ10 were identified as Aspergillus brunneoviolaceus, and isolate HZ123 was identified as Aspergillustubingensis. Furthermore, the results of in vivo PGP assays demonstrated that isolate HZ123 has a minimal negative effect on the growth of eggplant; however, the other three isolates, particularly isolate HZ06, caused the greatest increase in eggplant biomasses. Overall, these results indicate that isolate HZ06 has great potential as a PGP fungus to develop biofertilizer for application in eggplant production in immature soil from new reclamation land. Full article

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

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25 pages, 414 KiB

Open AccessEditor’s ChoiceReview

Genetic Markers Associated with Milk Production Traits in Dairy Cattle

by Yulin Ma, Muhammad Zahoor Khan, Jianxin Xiao, Gibson Maswayi Alugongo, Xu Chen, Tianyu Chen, Shuai Liu, Zhiyuan He, Jingjun Wang, Muhammad Kamal Shah and Zhijun Cao

Agriculture 2021, 11(10), 1018; https://doi.org/10.3390/agriculture11101018 - 18 Oct 2021

Cited by 20 | Viewed by 12232

Abstract

Increasing milk production is one of the key concerns in animal production. Traditional breeding has gotten limited achievement in the improvement of milk production because of its moderate heritability. Milk production traits are controlled by many genes. Thus, identifying candidate genes associated with [...] Read more.

Increasing milk production is one of the key concerns in animal production. Traditional breeding has gotten limited achievement in the improvement of milk production because of its moderate heritability. Milk production traits are controlled by many genes. Thus, identifying candidate genes associated with milk production traits may provide information that can be used to enhance the accuracy of animal selection for moderately heritable traits like milk production. The genomic selection can enhance the accuracy and intensity of selection and shortening the generation interval. The genetic progress of economically important traits can be doubled with the accuracy of selection and shortening of generation interval. Genome-wide association studies (GWAS) have made possible the screening of several single nucleotide polymorphisms (SNPs) in genes associated with milk production traits in dairy cattle. In addition, RNA-sequencing is another well-established tool used to identify genes associated with milk production in dairy cattle. Although it has been widely accepted that these three methods (GWAS, RNA-seq and DNA sequencing) are considered the first step in the screening of genes, however, the outcomes from GWAS, DNA-sequencing and RNA-seq still need further verification for the establishment of bonafide causal variants via genetic replication as well as functional validation. In the current review, we have highlighted genetic markers identified (2010-to date) for their associations with milk production traits in dairy cattle. The information regarding candidate genes associated with milk production traits provided in the current review could be helpful to select the potential genetic markers for the genetic improvement of milk production traits in dairy cattle. Full article

(This article belongs to the Special Issue Livestock Breeding and Conservation Genetics)

16 pages, 4683 KiB

Open AccessEditor’s ChoiceArticle

An Octopus-Inspired Bionic Flexible Gripper for Apple Grasping

by Jie Pi, Jun Liu, Kehong Zhou and Mingyan Qian

Agriculture 2021, 11(10), 1014; https://doi.org/10.3390/agriculture11101014 - 17 Oct 2021

Cited by 23 | Viewed by 5297

Abstract

When an octopus grasps something, the rigidity of its tentacle can change greatly, which allowing for unlimited freedom, agility, and precision. Inspired by this, a three-finger flexible bionic robot gripper was designed for apple picking. First, a flexible chamber finger was designed to [...] Read more.

When an octopus grasps something, the rigidity of its tentacle can change greatly, which allowing for unlimited freedom, agility, and precision. Inspired by this, a three-finger flexible bionic robot gripper was designed for apple picking. First, a flexible chamber finger was designed to drive the gripper finger to elongate, shorten, and bend, which works through a process of inflating and deflating. Further, we proposed a three-finger mode to achieve two kinds of motion states: grasping and relaxing, by simulating the movement of an octopus grasping at something. In this paper, we evaluated the bending property of the designed flexible bionic gripper through an apple grasping experiment. The experimental results show that the 100.0 g bionic gripper can load an apple with a weight of 246.5~350.0 g and a diameter of 69.0~99.0 mm, and the grasping success rate is 100%. It has a good grasping performance. Compared to other soft grippers, the proposed bionic flexible gripper has the advantages of being lightweight, and having good cushioning, low driving air pressure, and a strong grasping force. Full article

(This article belongs to the Special Issue Agricultural Structures and Mechanization)

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15 pages, 3060 KiB

Open AccessEditor’s ChoiceArticle

The Effect of Lignin Composition on Ruminal Fiber Fractions Degradation from Different Roughage Sources in Water Buffalo (Bubalus bubalis)

by Huimin Zhong, Jiayan Zhou, Mohamed Abdelrahman, Hao Xu, Zian Wu, Luncheng Cui, Zhenhua Ma, Liguo Yang and Xiang Li

Agriculture 2021, 11(10), 1015; https://doi.org/10.3390/agriculture11101015 - 17 Oct 2021

Cited by 17 | Viewed by 4963

Abstract

The water buffalo (Bubalus bubalis) is known for its unique utilization of low-quality fibrous feeds and outstanding digestion performance, highlighting its role as an animal model in studying fiber fractions degradation. Among roughage, lignin attracted wide attention in ruminant nutrition studies, [...] Read more.

The water buffalo (Bubalus bubalis) is known for its unique utilization of low-quality fibrous feeds and outstanding digestion performance, highlighting its role as an animal model in studying fiber fractions degradation. Among roughage, lignin attracted wide attention in ruminant nutrition studies, which affects animal digestibility. Therefore, the present study aims to investigate the functional relation between three lignin monomeric compositions of coniferyl alcohol (G), ρ-coumaryl alcohol (H) and sinapyl alcohol (S) and ruminal fiber degradation in water buffalo. Hence, three female water buffaloes (Nili-Ravi × Mediterranean, five years old, 480 ± 20 kg) were assigned for an in vivo study by utilizing the nylon-bag method, examining eight kinds of roughage. All the experimental roughage types were analyzed for the effective degradability (ED) of neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), cellulose (CEL) and hemicellulose (HC) fractions. Then, prediction models for the roughage fiber degradation were established based on the characteristics of lignin monomer content. The results showed that S, S/G and S/(G+S+H) were positively correlated with the ED of NDF, ADF, CEL and HC; H/S was negatively correlated. For the effective degradability of ADL (ADLD), S and S/(G+S+H) were positively correlated with it; H, H/G, H/S and H/(G+S+H) were negatively correlated. The model with the highest fitting degree was ADLD = 0.161 − 1.918 × H + 3.152 × S (R² = 0.758, p < 0.01). These results indicated that the lignin monomer composition is closely related to the utilization rate of roughage fiber. S-type lignin monomer plays a vital role in the fiber degradation of roughage. The experiment found the effect of lignin monomer composition on the degradation of fiber fractions using buffalo as the experimental animal and constructed prediction models, providing a scientific basis for building a new technological method using lignin composition to evaluate buffalo roughage. Furthermore, the capacity of ADL degradation of buffalo was proved in this experiment. In order to further explore the ability of lignin degradation by the buffalo, the DNA of rumen microorganisms was extracted for sequencing. The top three composition of rumen microorganisms at the genus level were Prevotella_1, 226, Rikenellaceae_RC9_gut_group and Ruminococcaceae_UCG-011. Six strains with lignin degradation ability were screened from buffalo rumen contents. This experiment also revealed that the buffalos possess rumen microorganisms with lignin degradation potential. Full article

(This article belongs to the Section Farm Animal Production)

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15 pages, 4714 KiB

Open AccessEditor’s ChoiceArticle

Method for Estimating Canopy Thickness Using Ultrasonic Sensor Technology

by Huitao Zhou, Weidong Jia, Yong Li and Mingxiong Ou

Agriculture 2021, 11(10), 1011; https://doi.org/10.3390/agriculture11101011 - 16 Oct 2021

Cited by 12 | Viewed by 3135

Abstract

The accurate detection of canopy characteristics is the basis of precise variable spraying. Canopy characteristics such as canopy density, thickness and volume are needed to vary the pesticide application rate and adjust the spray flow rate and air supply volume. Canopy thickness is [...] Read more.

The accurate detection of canopy characteristics is the basis of precise variable spraying. Canopy characteristics such as canopy density, thickness and volume are needed to vary the pesticide application rate and adjust the spray flow rate and air supply volume. Canopy thickness is an important canopy dimension for the calculation of tree canopy volume in pesticide variable spraying. With regard to the phenomenon of ultrasonic waves with multiple reflections and the further analysis of echo signals, we found that there is a proportional relationship between the canopy thickness and echo interval time. In this paper, we propose a method to calculate canopy thickness using echo signals that come from ultrasonic sensors. To investigate the application of this method, we conducted a set of lab-based experiments with a simulated canopy. The results show that we can accurately estimate canopy thickness when the detection distance, canopy density, and canopy thickness range between 0.5and 1.5 m, 1.2 and 1.4, and 0.3and 0.6 m, respectively. The relative error between the estimated value and actual value of the simulated canopy thickness is no higher than 8.8%. To compare our lab results with trees in the field, we measured canopy thickness from three naturally occurring Osmanthus trees (Osmanthus fragrans Lour). The results showed that the mean relative errors of three Osmanthus trees are 19.2%, 19.4% and 18.8%, respectively. These results can be used to improve measurements for agricultural production that includes both orchards and facilities by providing a reference point for the precise application of variable spraying. Full article

(This article belongs to the Section Agricultural Technology)

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20 pages, 610 KiB

Open AccessEditor’s ChoiceArticle

Know the Farmer That Feeds You: A Cross-Country Analysis of Spatial-Relational Proximities and the Attractiveness of Community Supported Agriculture

by Christina Gugerell, Takeshi Sato, Christine Hvitsand, Daichi Toriyama, Nobuhiro Suzuki and Marianne Penker

Agriculture 2021, 11(10), 1006; https://doi.org/10.3390/agriculture11101006 - 14 Oct 2021

Cited by 18 | Viewed by 4001

Abstract

While food production and consumption processes worldwide are characterized by geographical and social distance, alternative food networks aim to reconnect producers and consumers. Our study proposes a framework to distinguish multiple dimensions of proximity in the context of Community Supported Agriculture (a type [...] Read more.

While food production and consumption processes worldwide are characterized by geographical and social distance, alternative food networks aim to reconnect producers and consumers. Our study proposes a framework to distinguish multiple dimensions of proximity in the context of Community Supported Agriculture (a type of alternative food network) and to quantitatively evaluate them. In a principal component analysis, we aggregated various detailed proximity items from a multinational survey using principal component analysis and examined their relationship with the attractiveness of Community Supported Agriculture in a multiple regression analysis. Our findings highlight the importance of relational proximity and thus of increasing trust, collaboration, and the sharing of values and knowledge within and across organizations in the food system. Rather than focusing on spatial proximity, increasing relational proximity might support alternative food networks, such as Community Supported Agriculture. Full article

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

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16 pages, 7738 KiB

Open AccessEditor’s ChoiceArticle

Counting Dense Leaves under Natural Environments via an Improved Deep-Learning-Based Object Detection Algorithm

by Shenglian Lu, Zhen Song, Wenkang Chen, Tingting Qian, Yingyu Zhang, Ming Chen and Guo Li

Agriculture 2021, 11(10), 1003; https://doi.org/10.3390/agriculture11101003 - 14 Oct 2021

Cited by 15 | Viewed by 3429

Abstract

The leaf is the organ that is crucial for photosynthesis and the production of nutrients in plants; as such, the number of leaves is one of the key indicators with which to describe the development and growth of a canopy. The irregular shape [...] Read more.

The leaf is the organ that is crucial for photosynthesis and the production of nutrients in plants; as such, the number of leaves is one of the key indicators with which to describe the development and growth of a canopy. The irregular shape and distribution of the blades, as well as the effect of natural light, make the segmentation and detection process of the blades difficult. The inaccurate acquisition of plant phenotypic parameters may affect the subsequent judgment of crop growth status and crop yield. To address the challenge in counting dense and overlapped plant leaves under natural environments, we proposed an improved deep-learning-based object detection algorithm by merging a space-to-depth module, a Convolutional Block Attention Module (CBAM) and Atrous Spatial Pyramid Pooling (ASPP) into the network, and applying the smooth_L1 function to improve the loss function of object prediction. We evaluated our method on images of five different plant species collected under indoor and outdoor environments. The experimental results demonstrated that our algorithm which counts dense leaves improved average detection accuracy of 85% to 96%. Our algorithm also showed better performance in both detection accuracy and time consumption compared to other state-of-the-art object detection algorithms. Full article

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16 pages, 2839 KiB

Open AccessEditor’s ChoiceArticle

A Comparative Study of Semantic Segmentation Models for Identification of Grape with Different Varieties

by Yun Peng, Aichen Wang, Jizhan Liu and Muhammad Faheem

Agriculture 2021, 11(10), 997; https://doi.org/10.3390/agriculture11100997 - 13 Oct 2021

Cited by 24 | Viewed by 4220

Abstract

Accurate fruit segmentation in images is the prerequisite and key step for precision agriculture. In this article, aiming at the segmentation of grape cluster with different varieties, 3 state-of-the-art semantic segmentation networks, i.e., Fully Convolutional Network (FCN), U-Net, and DeepLabv3+ applied on six [...] Read more.

Accurate fruit segmentation in images is the prerequisite and key step for precision agriculture. In this article, aiming at the segmentation of grape cluster with different varieties, 3 state-of-the-art semantic segmentation networks, i.e., Fully Convolutional Network (FCN), U-Net, and DeepLabv3+ applied on six different datasets were studied. We investigated: (1) the segmentation performance difference of the 3 studied networks; (2) The impact of different input representations on segmentation performance; (3) The effect of image enhancement method to improve the poor illumination of images and further improve the segmentation performance; (4) The impact of the distance between grape clusters and camera on segmentation performance. The experiment results show that compared with FCN and U-Net the DeepLabv3+ combined with transfer learning is more suitable for the task with an intersection over union (IoU) of 84.26%. Five different input representations, namely RGB, HSV, L*a*b, HHH, and YCrCb obtained different IoU, ranging from 81.5% to 88.44%. Among them, the L*a*b got the highest IoU. Besides, the adopted Histogram Equalization (HE) image enhancement method could improve the model’s robustness against poor illumination conditions. Through the HE preprocessing, the IoU of the enhanced dataset increased by 3.88%, from 84.26% to 88.14%. The distance between the target and camera also affects the segmentation performance, no matter in which dataset, the closer the distance, the better the segmentation performance was. In a word, the conclusion of this research provides some meaningful suggestions for the study of grape or other fruit segmentation. Full article

(This article belongs to the Section Digital Agriculture)

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13 pages, 2060 KiB

Open AccessEditor’s ChoiceArticle

Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVM

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

Agriculture 2021, 11(10), 998; https://doi.org/10.3390/agriculture11100998 - 13 Oct 2021

Cited by 33 | Viewed by 2886

Abstract

Fusarium head blight, caused by a fungus, can cause quality deterioration and severe yield loss in wheat. It produces highly toxic deoxynivalenol, which is harmful to human and animal health. In order to quickly and accurately detect the severity of fusarium head blight, [...] Read more.

Fusarium head blight, caused by a fungus, can cause quality deterioration and severe yield loss in wheat. It produces highly toxic deoxynivalenol, which is harmful to human and animal health. In order to quickly and accurately detect the severity of fusarium head blight, a method of detecting the disease using continuous wavelet analysis and particle swarm optimization support vector machines (PSO-SVM) is proposed in this paper. First, seven wavelet features for fusarium head blight detection were extracted using continuous wavelet analysis based on the hyperspectral reflectance of wheat ears. In addition, 16 traditional spectral features were selected using correlation analysis, including two continuous removal transformed spectral features, six differential spectral features, and eight vegetation indices. Finally, wavelet features and traditional spectral features were used as input features to construct fusarium head blight detection models in combination with the PSO-SVM algorithm, and the results were compared with those obtained using random forest (RF) and a back propagation neural network (BPNN). The results show that, under the same feature variables, the PSO-SVM detection method gave an overall higher accuracy than the BPNN detection method, while the overall accuracy of the RF detection model was the lowest. The overall accuracy of the RF, BPNN and PSO-SVM detection models with wavelet features was higher by 3.7%, 2.9% and 8.3% compared to the corresponding methodological models with traditional spectral features. The detection model with wavelet features combining the PSO-SVM algorithm gave the highest overall accuracies (93.5%) and kappa coefficients (0.903) in the six monitoring models. These results suggest that the PSO-SVM algorithm combined with continuous wavelet analysis can significantly improve the accuracy of fusarium head blight detection on the wheat ears scale. Full article

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16 pages, 14823 KiB

Open AccessEditor’s ChoiceArticle

Review on Multitemporal Classification Methods of Satellite Images for Crop and Arable Land Recognition

by Joanna Pluto-Kossakowska

Agriculture 2021, 11(10), 999; https://doi.org/10.3390/agriculture11100999 - 13 Oct 2021

Cited by 21 | Viewed by 4361

Abstract

This paper presents a review of the conducted research in the field of multitemporal classification methods used for the automatic identification of crops and arable land using optical satellite images. The review and systematization of these methods in terms of the effectiveness of [...] Read more.

This paper presents a review of the conducted research in the field of multitemporal classification methods used for the automatic identification of crops and arable land using optical satellite images. The review and systematization of these methods in terms of the effectiveness of the obtained results and their accuracy allows for the planning towards further development in this area. The state of the art analysis concerns various methodological approaches, including selection of data in terms of spatial resolution, selection of algorithms, as well as external conditions related to arable land use, especially the structure of crops. The results achieved with use of various approaches and classifiers and subsequently reported in the literature vary depending on the crops and area of analysis and the sources of satellite data. Hence, their review and systematic conclusions are needed, especially in the context of the growing interest in automatic processes of identifying crops for statistical purposes or monitoring changes in arable land. The results of this study show no significant difference between the accuracy achieved from different machine learning algorithms, yet on average artificial neural network classifiers have results that are better by a few percent than others. For very fragmented regions, better results were achieved using Sentinel-2, SPOT-5 rather than Landsat images, but the level of accuracy can still be improved. For areas with large plots there is no difference in the level of accuracy achieved from any HR images. Full article

(This article belongs to the Special Issue Image Analysis Techniques in Agriculture)

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13 pages, 4496 KiB

Open AccessEditor’s ChoiceArticle

Shelterbelt Structure and Crop Protection from Increased Typhoon Activity in Northeast China

by Xuelu Cai, Mark Henderson, Ligang Wang, Yuanhang Su and Binhui Liu

Agriculture 2021, 11(10), 995; https://doi.org/10.3390/agriculture11100995 - 13 Oct 2021

Cited by 11 | Viewed by 5794

Abstract

Global warming has led to increases in the frequency and intensity of typhoons. In recent years, super typhoons have had a greater impact on agriculture in the black soil farmland of Northeast China, posing serious threats to crop growth. Planting trees as windbreaks [...] Read more.

Global warming has led to increases in the frequency and intensity of typhoons. In recent years, super typhoons have had a greater impact on agriculture in the black soil farmland of Northeast China, posing serious threats to crop growth. Planting trees as windbreaks and to reduce erosion is common in this region, but their protective effects against crop damage from typhoons is still unknown. This paper studied the protective effect of different shelterbelt structures on crops that encountered a super typhoon. The results show that the distance between shelterbelt rows and shelterbelt porosity have significant influences on the starting lodging distance of crops behind the shelterbelt. Increasing the shelterbelt distance between shelterbelt rows or reducing shelterbelt porosity can enhance their protective effects on crops. Among the main crops, rice has the strongest lodging resistance, followed by soybeans, with maize being the least resistant. The protective effect of mixed tree and shrub shelterbelts is better than that of single tree species shelterbelts. Dead or missing trees reduce the shelterbelt protective effect. These results provide strategies for reducing the impact of more intense and frequent super typhoons. Full article

(This article belongs to the Section Agricultural Systems and Management)

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21 pages, 2278 KiB

Open AccessEditor’s ChoiceArticle

Physiological Screening for Drought Tolerance Traits in Vegetable Amaranth (Amaranthus tricolor) Germplasm

by Norain Jamalluddin, Festo J. Massawe, Sean Mayes, Wai Kuan Ho, Ajit Singh and Rachael C. Symonds

Agriculture 2021, 11(10), 994; https://doi.org/10.3390/agriculture11100994 - 13 Oct 2021

Cited by 16 | Viewed by 4509

Abstract

Amaranth (Amaranthus tricolor), an underutilized climate smart crop, is highly nutritious and possesses diverse drought tolerance traits, making it an ideal crop to thrive in a rapidly changing climate. Despite considerable studies on the growth and physiology of plants subjected to [...] Read more.

Amaranth (Amaranthus tricolor), an underutilized climate smart crop, is highly nutritious and possesses diverse drought tolerance traits, making it an ideal crop to thrive in a rapidly changing climate. Despite considerable studies on the growth and physiology of plants subjected to drought stress, a precise trait phenotyping strategy for drought tolerance in vegetable amaranth is still not well documented. In this study, two drought screening trials were carried out on 44 A. tricolor accessions in order to identify potential drought-tolerant A. tricolor germplasm and to discern their physiological responses to drought stress. The findings revealed that a change in stem biomass was most likely the main mechanism of drought adaptation for stress recovery, and dark-adapted quantum yield (Fv/Fm) could be a useful parameter for identifying drought tolerance in amaranth. Three drought tolerance indices: geometric mean productivity (GMP), mean productivity (MP) and stress tolerance index (STI) identified eight drought-tolerant accessions with stable performance across the two screening trials. The highly significant genotypic differences observed in several physiological traits among the amaranth accessions indicate that the amaranth panel used in this study could be a rich source of genetic diversity for breeding purposes for drought tolerance traits. Full article

(This article belongs to the Special Issue Vegetable Crops Breeding for Abiotic Stress Tolerance and Quality Traits)

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16 pages, 326 KiB

Open AccessEditor’s ChoiceReview

A Review on the Modified Atmosphere Preservation of Fruits and Vegetables with Cutting-Edge Technologies

by Yujie Fang and Minato Wakisaka

Agriculture 2021, 11(10), 992; https://doi.org/10.3390/agriculture11100992 - 12 Oct 2021

Cited by 56 | Viewed by 13611

Abstract

Respiration and microbial infection are important causes of postharvest spoilage of fruits and vegetables (F&V). Atmosphere storage technology can significantly reduce postharvest losses. This comprehensive review aims to cover recent progress in the application of atmosphere storage to F&V preservation, not only focusing [...] Read more.

Respiration and microbial infection are important causes of postharvest spoilage of fruits and vegetables (F&V). Atmosphere storage technology can significantly reduce postharvest losses. This comprehensive review aims to cover recent progress in the application of atmosphere storage to F&V preservation, not only focusing on the effect of gas conditions but also evaluating combination applications involving newer preservation technologies, including ethylene scavengers, high-pressure and decompression technology, ozone, ultraviolet radiation, active packaging, high-voltage electrostatic field, plasma treatment, and pulse-controlled atmosphere. Appropriate choice of storage conditions optimal for each F&V is essential since the physiological properties and sensory qualities are affected by them. The combination of atmosphere storage with these emerging technologies could contribute to significant reductions in food loss during storage. Full article

18 pages, 2348 KiB

Open AccessEditor’s ChoiceArticle

Effects of Exogenous Calcium on Adaptive Growth, Photosynthesis, Ion Homeostasis and Phenolics of Gleditsia sinensis Lam. Plants under Salt Stress

by Yun Guo, Yang Liu, Yan Zhang, Jia Liu, Zarmina Gul, Xiao-Rui Guo, Ann Abozeid and Zhong-Hua Tang

Agriculture 2021, 11(10), 978; https://doi.org/10.3390/agriculture11100978 - 9 Oct 2021

Cited by 26 | Viewed by 3436

Abstract

Salinity is the main environmental factor responsible for limited plant growth in many areas of the world. Gleditsia sinensis Lam. is a shelter forest tree species that does not require high-quality soil and can even grow in mild saline soil. This study mainly [...] Read more.

Salinity is the main environmental factor responsible for limited plant growth in many areas of the world. Gleditsia sinensis Lam. is a shelter forest tree species that does not require high-quality soil and can even grow in mild saline soil. This study mainly explored the tolerance of G. sinensis to salt and the effect of exogenous calcium addition on the growth of G. sinensis in a salinized soil. The concentrations of NaCl were set as 0 mmol/L, 100 mmol/L, and 200 mmol/L. Compared with the control, under the NaCl treatment of 200 mmol/L, it was observed that the leaves of G. sinensis turned yellow, the electrical conductivity significantly increased, and the water content and the chlorophyll content significantly decreased, which is probably unfavorable for growth. Our study showed that the addition of 10 mmol/L exogenous calcium chloride under salt stress had a positive effect on the growth and photosynthetic characteristics of G. sinensis. Moreover, the addition of exogenous calcium attenuated the cytotoxicity caused by Na⁺ under salt stress and promoted the equilibrium of ion homeostasis. More importantly, the addition of exogenous calcium ions was beneficial for the survival of G. sinensis plants on salinized land and the increase of effective active ingredient content including phenolic compounds, which is of direct significance for improving environmental problems such as desertification of saline-alkali land. In conclusion, we investigated the effect of salt treatment on G. sinensis, as well as the positive effects of exogenous calcium on the survival and growth of G. sinensis in salt environment, which provided a scientific basis for the targeted cultivation of G. sinensis in salinized land and the effective utilization of salinized and alkaline land. Full article

(This article belongs to the Special Issue Applications of Plant Growth Regulators and Hormones on Crop Resistance)

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14 pages, 4178 KiB

Open AccessEditor’s ChoiceArticle

Research on Loading Method of Tractor PTO Based on Dynamic Load Spectrum

by Yu Wang, Ling Wang, Jianhua Zong, Dongxiao Lv and Shumao Wang

Agriculture 2021, 11(10), 982; https://doi.org/10.3390/agriculture11100982 - 9 Oct 2021

Cited by 13 | Viewed by 2738

Abstract

The torque load spectrum is an important basis for the strength design and durability test verification of tractor power take-off (PTO), and the performance and reliability of tractor PTO directly affect the quality and efficiency of agricultural operations. In this paper, taking the [...] Read more.

The torque load spectrum is an important basis for the strength design and durability test verification of tractor power take-off (PTO), and the performance and reliability of tractor PTO directly affect the quality and efficiency of agricultural operations. In this paper, taking the PTO torque load as the object, a PTO loading method based on the dynamic load spectrum acquired in the actual field work was proposed in this paper. Based on the Peak Over Threshold model, the extrapolation of the PTO load spectrum was realized, and the load spectrum throughout the whole life cycle was obtained. On the basis of this, the mobile tractor PTO loading test bench and Fuzzy-Proportional-Integral-Derivative (Fuzzy-PID) controller were developed to achieve the dynamic loading of the PTO load spectrum, and the dynamic characteristics were analyzed and verified by the simulation and laboratory test. The results showed that with the time domain extrapolation method, the load extreme value was expanded from (63.24, 469.50) to (60.88, 475.18), and the coverage was expanded by 1.98%. By comparing with the fitting results, statistical characteristics and rain flow counting results, the load spectrum extrapolation method was effective. In addition, the response time of simulation and laboratory test were 0.05s and 0.75s, respectively; the maximum error was 1.77% and 4.03%, respectively; and the goodness of fit was 16.78 N·m, which indicated that the PTO loading test bench, can accurately restore the dynamic loading of the tractor and the Fuzzy-PID controller had better accuracy and stability. It would provide a reference for the practical application of PTO load spectrum of the tractors. Full article

(This article belongs to the Special Issue Agricultural Structures and Mechanization)

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16 pages, 1139 KiB

Open AccessEditor’s ChoiceArticle

Customer Preferences for Organic Agriculture Produce in the Czech Republic: 2016 and 2019

by Martina Zámková, Stanislav Rojík, Ladislav Pilař, Martina Chalupová, Martin Prokop, Radek Stolín, Paweł Dziekański and Mansoor Maitah

Agriculture 2021, 11(10), 968; https://doi.org/10.3390/agriculture11100968 - 6 Oct 2021

Cited by 11 | Viewed by 3352

Abstract

The article analyses the customer attitude towards the qualities and benefits of organic agriculture production for farmers and customers in the Czech Republic, comparing the situation in 2016 and 2019. More than 2500 respondents were subject to the marketing research in the years [...] Read more.

The article analyses the customer attitude towards the qualities and benefits of organic agriculture production for farmers and customers in the Czech Republic, comparing the situation in 2016 and 2019. More than 2500 respondents were subject to the marketing research in the years 2016 and 2019. The data were processed using correspondence analysis and logistic regression. The research study shows that the number of respondents who consider organic food is growing; at the same time, there is a rather large share of consumers who believe organic food to be of better quality. The results show a favourable change in the popularity of organic food. While, in 2016, the main decisive factor in shopping for organic food was its price, in 2019, the main criterion, for the respondents, was quality, with the criterion of price being complemented by the perception of organic food as healthier than conventional food. At the same time, it was established that, the amount spent on organic food in 2019 was higher than that in 2016. This finding was in positive correlation with the increase in respondents’ income. For farmers, organic farming is a promising alternative to conventional agriculture due to a rising demand for organic produce. Full article

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

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17 pages, 2774 KiB

Open AccessEditor’s ChoiceArticle

Nosema Ceranae Interactions with Nosema apis and Black Queen Cell Virus

by Anna Maria Gajda, Ewa Danuta Mazur, Andrzej Marcin Bober and Michał Czopowicz

Agriculture 2021, 11(10), 963; https://doi.org/10.3390/agriculture11100963 - 3 Oct 2021

Cited by 13 | Viewed by 3175

Abstract

Nosema ceranae is a relatively new pathogen of the honeybee (Apis mellifera) and the course of type C nosemosis (the disease that it causes) is not entirely known. In order to better understand the course and the consequences of this disease, [...] Read more.

Nosema ceranae is a relatively new pathogen of the honeybee (Apis mellifera) and the course of type C nosemosis (the disease that it causes) is not entirely known. In order to better understand the course and the consequences of this disease, laboratory experiments were performed. They aimed to compare the course of N. ceranae infection with the course of Nosema apis infection, taking its influence on the black queen cell virus (BQCV) into account. Determination of the quantity of N. ceranae and BQCV genetic material in laboratory tests was performed using real-time PCR. In mixed Nosema infections, N. ceranae “wins” the competition and manages to outnumber N. apis significantly. BQCV exacerbates the course of both A and C nosemoses, but the data shows that in the case of nosemosis C and this viral infection, the mortality rate was the highest from all examined groups. Obtained results show that N. ceranae is more pathogenic for A. mellifera than N. apis, and the course of type C nosemosis is much heavier, which results in the shortened life spans of bees, and in connection with BQCV it becomes even more dangerous to bees. Full article

(This article belongs to the Special Issue Emerging Problems of Modern Beekeeping)

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18 pages, 3763 KiB

Open AccessEditor’s ChoiceArticle

Research on Load Disturbance Based Variable Speed PID Control and a Novel Denoising Method Based Effect Evaluation of HST for Agricultural Machinery

by Zhun Cheng and Zhixiong Lu

Agriculture 2021, 11(10), 960; https://doi.org/10.3390/agriculture11100960 - 2 Oct 2021

Cited by 21 | Viewed by 2432

Abstract

This paper aims to realize and improve the constant speed control performance of tractors with HST (Hydrostatic Transmission) variable speed units. To achieve this, based on the HST test bench of the tractor, we perform a verification test of the adjustable speed characteristics, [...] Read more.

This paper aims to realize and improve the constant speed control performance of tractors with HST (Hydrostatic Transmission) variable speed units. To achieve this, based on the HST test bench of the tractor, we perform a verification test of the adjustable speed characteristics, a denoising filter test of the response signal, a test on the influence of the load disturbance on the adjustable speed characteristics and a PID-based constant speed performance detection test. The results of the verification test of the adjustable speed characteristics show that the theoretical value and actual value of the adjustable speed transmission characteristics of the HST used are essentially consistent with each other. The results of the test of the load disturbance’s influence on the adjustable speed characteristics show that the increase in load torque inhibits the HST output response. Therefore, the paper proposes and designs a PID-based closed-loop constant speed control system. The paper uses a step response test and a load disturbance test to research the control result of the constant speed system. Collecting and analyzing all test results, we find that the constant speed control based on PID has a very good result. The average error between the average HST output speed and the target speed set was 0.37%, and the average standard deviation of output speed was 1.18 rpm. In addition, the paper proposes a denoising method combing the empirical mode decomposition method and the Gaussian distribution determination. The method shows that the first two orders of the components of the HST response signal should be removed as noise. The paper uses the denoised signal and the partial least squares method to analyze the influencing factors of the constant speed control result. The analysis results show that the rate of change of load torque has the biggest influence on the stability of HST output speed, followed by the target value. Full article

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

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17 pages, 4262 KiB

Open AccessEditor’s ChoiceArticle

Autonomous and Safe Navigation of Mobile Robots in Vineyard with Smooth Collision Avoidance

by Abhijeet Ravankar, Ankit A. Ravankar, Arpit Rawankar and Yohei Hoshino

Agriculture 2021, 11(10), 954; https://doi.org/10.3390/agriculture11100954 - 30 Sep 2021

Cited by 22 | Viewed by 3963

Abstract

In recent years, autonomous robots have extensively been used to automate several vineyard tasks. Autonomous navigation is an indispensable component of such field robots. Autonomous and safe navigation has been well studied in indoor environments and many algorithms have been proposed. However, unlike [...] Read more.

In recent years, autonomous robots have extensively been used to automate several vineyard tasks. Autonomous navigation is an indispensable component of such field robots. Autonomous and safe navigation has been well studied in indoor environments and many algorithms have been proposed. However, unlike structured indoor environments, vineyards pose special challenges for robot navigation. Particularly, safe robot navigation is crucial to avoid damaging the grapes. In this regard, we propose an algorithm that enables autonomous and safe robot navigation in vineyards. The proposed algorithm relies on data from a Lidar sensor and does not require a GPS. In addition, the proposed algorithm can avoid dynamic obstacles in the vineyard while smoothing the robot’s trajectories. The curvature of the trajectories can be controlled, keeping a safe distance from both the crop and the dynamic obstacles. We have tested the algorithm in both a simulation and with robots in an actual vineyard. The results show that the robot can safely navigate the lanes of the vineyard and smoothly avoid dynamic obstacles such as moving people without abruptly stopping or executing sharp turns. The algorithm performs in real-time and can easily be integrated into robots deployed in vineyards. Full article

(This article belongs to the Section Agricultural Technology)

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22 pages, 852 KiB

Open AccessEditor’s ChoiceReview

The Fate of Nitrogen from Soil to Plants: Influence of Agricultural Practices in Modern Agriculture

by Maria Giordano, Spyridon A. Petropoulos and Youssef Rouphael

Agriculture 2021, 11(10), 944; https://doi.org/10.3390/agriculture11100944 - 29 Sep 2021

Cited by 23 | Viewed by 8572

Abstract

Nitrogen is an element present on Earth in different forms, such as gaseous in the air, dissolved in water, immobilized in the soil, as well as biologically bound in all living organisms. The transition from one form to another constitutes the nitrogen cycle. [...] Read more.

Nitrogen is an element present on Earth in different forms, such as gaseous in the air, dissolved in water, immobilized in the soil, as well as biologically bound in all living organisms. The transition from one form to another constitutes the nitrogen cycle. Current agricultural systems rely on nitrogen fertilizers, which represent the reactive or biologically available nitrogen in soil. The excessive presence of reactive nitrogen in the environment has become a threat to soil, water, and air. The increasing demands for food in the world are associated with significant increase in nitrogen fertilizers inputs which threatens the environment and living organisms. The quantities of nitrogen used per capita in developed countries exceed those in developing countries. However, developed countries are regulated by restrictions of fertilizers inputs in agriculture, whereas such regulations do not exist in most of the developing countries. The need to resort to alternative and eco-sustainable strategies to mitigate the pollution related to human activities, is increasingly evident. This review aims to highlight the fate of nitrogen through the main agricultural practices in modern agriculture. Special attention was given to rocket (Eruca sativa) which is considered a nitrate hyper-accumulator and was used as a case study in the present review. Finally, some eco-sustainable solutions, useful for mitigating or preventing the excessive release of harmful forms of nitrogen into the environment, were also discussed. Full article

(This article belongs to the Section Crop Production)

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15 pages, 1164 KiB

Open AccessEditor’s ChoiceArticle

The Role of Cooperatives in Brazilian Agricultural Production

by Mateus de Carvalho Reis Neves, Felipe de Figueiredo Silva, Carlos Otávio de Freitas and Marcelo José Braga

Agriculture 2021, 11(10), 948; https://doi.org/10.3390/agriculture11100948 - 29 Sep 2021

Cited by 11 | Viewed by 5765

Abstract

Much of the established literature on agricultural cooperatives describes their myriad contributions to farmers’ economic performance. In Brazil, one of the world’s leading agricultural exporters, there were more than 1500 agricultural cooperatives with 1 million members in 2020, and in 2017, 11% of [...] Read more.

Much of the established literature on agricultural cooperatives describes their myriad contributions to farmers’ economic performance. In Brazil, one of the world’s leading agricultural exporters, there were more than 1500 agricultural cooperatives with 1 million members in 2020, and in 2017, 11% of all Brazilian farms were associated with one of these cooperatives. In this paper, we estimate the factors associated with the municipality share of cooperative membership (MSCM) and how municipality-level production value changes with MSCM. Our analysis is at the municipality level using aggregate data from the 2017 Agricultural Census. We find that in Brazil, higher education and smaller property sizes are associated with membership in agricultural cooperatives. To estimate how MSCM is associated with farm profits, we use a generalized propensity score and find that an increase in MSCM increases net municipal farm income, driven mostly by an increase in the value of agricultural production compared to a smaller increase in the cost of production. Full article

(This article belongs to the Special Issue Agricultural Cooperative in the Face of the Challenges of Globalization, Sustainability and Digitalization)

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20 pages, 2431 KiB

Open AccessEditor’s ChoiceArticle

Magnitude, Causes and Scope for Reducing Food Losses in the Baking and Confectionery Industry—A Multi-Method Approach

by Elżbieta Goryńska-Goldmann, Michał Gazdecki, Krystyna Rejman, Sylwia Łaba, Joanna Kobus-Cisowska and Krystian Szczepański

Agriculture 2021, 11(10), 936; https://doi.org/10.3390/agriculture11100936 - 28 Sep 2021

Cited by 24 | Viewed by 9197

Abstract

Reducing food wastage is one of the challenges in achieving global food security and transforming current food systems. Since human nutrition is closely dependent on cereal production, research was undertaken aimed at understanding the food losses in the baking and confectionery industry (BCI) [...] Read more.

Reducing food wastage is one of the challenges in achieving global food security and transforming current food systems. Since human nutrition is closely dependent on cereal production, research was undertaken aimed at understanding the food losses in the baking and confectionery industry (BCI) in Poland, in particular at determining the volume, reasons and ways of reducing losses, identifying possibly all of the reasons for losses in BCI using the Ishikawa 5M + 1E diagram and determining the level of significance and probability of risk of food losses in the analysed sector. Two research methods were used. Quantitative data were collected using the mass balance method from five businesses that served as case studies. Qualitative data were collected through individual in-depth interviews with 17 industry experts. The companies’ average daily losses ranged from 0.8 to 6.4 tons, representing 9.7 to 14.4% of production volume, including 10.4–13.4% of bread losses and 6.8–24.4% of fresh pastry losses. The highest losses were generated by transport departments and these were exclusively retail returns. Following the Ishikawa concept, 31 primary and 94 secondary reasons for food losses were identified. Using the probability of loss risk, a toolkit for loss prevention and mitigation across all departments within businesses (raw materials magazine, production section, final product magazine and final product transport) and a set of horizontal tools were identified, including specialised training for employees and activities in several areas, e.g., technical status and production technology, organisation and planning, logistics and sales and cooperation with retail. This study, conducted in Poland, offers valuable results for developing programmes and strategies to prevent and manage food losses in BCI. Many of the solutions proposed in both toolkits can bring economic benefits without involving additional high costs. Full article

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

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23 pages, 1899 KiB

Open AccessEditor’s ChoiceArticle

Evolutionary Subdivision of Domestic Chickens: Implications for Local Breeds as Assessed by Phenotype and Genotype in Comparison to Commercial and Fancy Breeds

by Tatyana A. Larkina, Olga Y. Barkova, Grigoriy K. Peglivanyan, Olga V. Mitrofanova, Natalia V. Dementieva, Olga I. Stanishevskaya, Anatoly B. Vakhrameev, Alexandra V. Makarova, Yuri S. Shcherbakov, Marina V. Pozovnikova, Evgeni A. Brazhnik, Darren K. Griffin and Michael N. Romanov

Agriculture 2021, 11(10), 914; https://doi.org/10.3390/agriculture11100914 - 24 Sep 2021

Cited by 17 | Viewed by 3544

Abstract

To adjust breeding programs for local, commercial, and fancy breeds, and to implement molecular (marker-assisted) breeding, a proper comprehension of phenotypic and genotypic variation is a sine qua non for breeding progress in animal production. Here, we investigated an evolutionary subdivision of domestic [...] Read more.

To adjust breeding programs for local, commercial, and fancy breeds, and to implement molecular (marker-assisted) breeding, a proper comprehension of phenotypic and genotypic variation is a sine qua non for breeding progress in animal production. Here, we investigated an evolutionary subdivision of domestic chickens based on their phenotypic and genotypic variability using a wide sample of 49 different breeds/populations. These represent a significant proportion of the global chicken gene pool and all major purposes of breed use (according to their traditional classification model), with many of them being characterized by a synthetic genetic structure and notable admixture. We assessed their phenotypic variability in terms of body weight, body measurements, and egg production. From this, we proposed a phenotypic clustering model (PCM) including six evolutionary lineages of breed formation: egg-type, meat-type, dual purpose (egg-meat and meat-egg), game, fancy, and Bantam. Estimation of genotypic variability was carried out using the analysis of five SNPs, i.e., at the level of genomic variation at the NCAPG-LCORL locus. Based on these data, two generally similar genotypic clustering models (GCM1 and GCM2) were inferred that also had several overlaps with PCM. Further research for SNPs associated with economically important traits can be instrumental in marker-assisted breeding programs. Full article

(This article belongs to the Special Issue Poultry: Breeding, Health, Nutrition, and Management)

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22 pages, 41809 KiB

Open AccessEditor’s ChoiceArticle

Optimization Design and Experiment of Ear-Picking and Threshing Devices of Corn Plot Kernel Harvester

by Ranbing Yang, Dongquan Chen, Xiantao Zha, Zhiguo Pan and Shuqi Shang

Agriculture 2021, 11(9), 904; https://doi.org/10.3390/agriculture11090904 - 21 Sep 2021

Cited by 14 | Viewed by 4185

Abstract

In order to solve the problems of easy-to-break kernels and substantial harvest losses during kernel harvesting in breeding trials plot of corn, an ear-picking device and a threshing device of corn plot kernel harvester has been optimized. To automatically change the gap of [...] Read more.

In order to solve the problems of easy-to-break kernels and substantial harvest losses during kernel harvesting in breeding trials plot of corn, an ear-picking device and a threshing device of corn plot kernel harvester has been optimized. To automatically change the gap of the ear-picking plate, a self-elastic structure with compression spring and connecting rod is used. The ear-picking plate is glued, and an elastic rubber gasket is placed underneath it, which effectively improves the adaptability of the ear-picking device and reduces corn kernel collision damage during ear-picking. To ensure the self-purification of the ear-picking device, a combination of auger sieve hole cleaning device and lateral pneumatic auxiliary cleaning system is used. A dual-axial flow threshing device is designed, which uses a “U”-shaped conveying system to transport maize ears in the threshing chamber. The spacing of the concave sieve may be adjusted, and the residual kernels in the threshing chamber can be cleaned up after harvesting one plot by combining three cleanings, which meets the requirements of no mixing between plots. The force analysis of corn ears in the threshing chamber determines the best design plan for the forward speed, the speed of the second threshing drum, and the threshing gap. The breakage rate and non-threshing rate regression models were created using the quadratic regression orthogonal combination test, and the parameters were optimized using MATLAB. The verification test results showed that when the forward speed was 0.61 m/s, the second threshing drum speed was 500 r/min, and the threshing gap was 40 mm, the breakage rate was 1.47%, and the non-threshing rate was 0.89%, which met the kernel harvesting requirements in corn plots. Full article

(This article belongs to the Special Issue Agricultural Structures and Mechanization)

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23 pages, 3112 KiB

Open AccessEditor’s ChoiceArticle

Soil Particulate and Mineral-Associated Organic Matter Increases in Organic Farming under Cover Cropping and Manure Addition

by Karin Kauer, Sandra Pärnpuu, Liina Talgre, Viacheslav Eremeev and Anne Luik

Agriculture 2021, 11(9), 903; https://doi.org/10.3390/agriculture11090903 - 19 Sep 2021

Cited by 16 | Viewed by 6006

Abstract

This study aimed to investigate the soil organic carbon (SOC) sequestration rate and soil organic matter (SOM) composition in conventional rotational cropping with mineral fertilization compared with organic cover cropping with and without composted manure addition during 2008–2018 to specify the SOM stabilization [...] Read more.

This study aimed to investigate the soil organic carbon (SOC) sequestration rate and soil organic matter (SOM) composition in conventional rotational cropping with mineral fertilization compared with organic cover cropping with and without composted manure addition during 2008–2018 to specify the SOM stabilization under different farming systems. The SOC proportion in particulate organic matter (POM) (63–2000 µm) and mineral-associated organic matter (MAOM) (<63 µm) fractions were estimated in different treatments, and the SOM composition in the fractions was characterized by FTIR spectroscopy. The SOC sequestration rate was treatment-dependent, with the higher SOC sequestration rate (1.26 Mg ha⁻¹ y⁻¹) in the organic treatment with cover crop and composted manure. Across all treatments, 57.3%–77.8% of the SOC stock was in the MAOM fraction. Mineral N fertilization increased POM-C concentration by 19%–52% compared with the unfertilized control. Under the organic treatments, the POM-C concentration was 83%–95% higher than the control. The MAOM-C concentration increased by 8%–20%. The mineral N fertilization and organic treatments (with and without cover crops and composted manure) increased the SOC stock proportion of POM. The highest proportion of SOC stock related to POM was in the cover cropping system, reducing the proportion of C related to the MAOM fraction, but the addition of composted manure with cover cropping also increased the proportion of C in MAOM. Compared with MAOM, the POM had a less resistant organic matter composition, and the POM resistance was higher in organic than conventional treatments. In general, the recalcitrance of SOM increased with SOC concentration. The POM fraction had higher aromaticity (or degree of decomposition) than the MAOM fraction. The aromaticity in POM and MAOM fractions was higher in the organic farming system and depended on mineral N fertilization and cover cropping, but the effect of manure was not significant. Although the SOC sequestration rate was higher under manure addition, resulting in the highest formation of both POM and MAOM in the soil, manure addition had little effect on overall SOM composition compared with cover crops. Full article

(This article belongs to the Section Agricultural Soils)

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18 pages, 814 KiB

Open AccessEditor’s ChoiceReview

Effects of Triazole Fungicides on Soil Microbiota and on the Activities of Enzymes Found in Soil: A Review

by Diana Larisa Roman, Denisa Ioana Voiculescu, Madalina Filip, Vasile Ostafe and Adriana Isvoran

Agriculture 2021, 11(9), 893; https://doi.org/10.3390/agriculture11090893 - 17 Sep 2021

Cited by 66 | Viewed by 9001

Abstract

Triazole fungicides can manifest toxicity to a wide range of non-target organisms. Within this study we present a systematic review of the effects produced on the soil microbiota and activity of soil enzymes by the following triazole fungicides: cyproconazole, difenoconazole, epoxiconazole, flutriafol, hexaconazole, [...] Read more.

Triazole fungicides can manifest toxicity to a wide range of non-target organisms. Within this study we present a systematic review of the effects produced on the soil microbiota and activity of soil enzymes by the following triazole fungicides: cyproconazole, difenoconazole, epoxiconazole, flutriafol, hexaconazole, metconazole, myclobutanil, paclobutrazole, propiconazole, tebuconazole, tetraconazole, triadimenol, triadimefon, and triticonazole. Known effects of the triazole fungicides on the soil activity are dose dependent. High doses of triazole fungicides strongly affects the structure of the microbial communities in soil and usually decrease the soil microbial population and the activities of enzymes found in soil. Full article

(This article belongs to the Section Agricultural Soils)

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15 pages, 563 KiB

Open AccessEditor’s ChoiceArticle

The Influence of New Agricultural Business Entities on the Economic Welfare of Farmer’s Families

by Lingjuan Cheng, Wei Zou and Kaifeng Duan

Agriculture 2021, 11(9), 880; https://doi.org/10.3390/agriculture11090880 - 14 Sep 2021

Cited by 14 | Viewed by 3571

Abstract

Promoting the coordinated development of new agricultural business entities and small farmers is an important way to realize rural revitalization. It is undoubtedly of great significance to clarify the impact and its mechanism of new agricultural business entities on the economic welfare of [...] Read more.

Promoting the coordinated development of new agricultural business entities and small farmers is an important way to realize rural revitalization. It is undoubtedly of great significance to clarify the impact and its mechanism of new agricultural business entities on the economic welfare of farmers’ families. Based on the 2015 China Household Finance Survey (CHFS) data, this paper builds a theoretical analytical framework of “new agricultural business entities—non-agricultural employment and agricultural output—economic welfare of farmers’ family”. From the intermediary perspective of the non-agricultural employment and agricultural output, it empirically tests the impact of new agricultural business entities on the economic welfare of farmers’ families by combining the analysis methods of the benchmark regression and intermediary effect. The research shows that: (1) New agricultural business entities promote the improvement of the economic welfare of farmers’ families. The specific manifestation is that the existence of new agricultural business entities can not only increase the per capita annual income of farmers’ families, but also promote the per capita consumption expenditure of farmers’ families in the village. (2) Non-agricultural employment and agricultural output have a significant mediating effect in the impact of new agricultural business entities on the economic welfare of farmers’ families. (3) In addition to key variables, variables such as education, political status, and family status are also key factors affecting the economic welfare of farmers’ families. Finally, this paper puts forward some policy recommendations such as cultivating high-quality new agricultural business entities, strengthening farmers’ technical training, and optimizing rural residents’ policies. Full article

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

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17 pages, 964 KiB

Open AccessEditor’s ChoiceReview

Potentiality of Vermicomposting in the South Pacific Island Countries: A Review

by Randy Carlie Pierre-Louis, Md. Abdul Kader, Nandakumar M Desai and Eleanor H John

Agriculture 2021, 11(9), 876; https://doi.org/10.3390/agriculture11090876 - 13 Sep 2021

Cited by 18 | Viewed by 9266

Abstract

Incorporation of vermin culture in the composting system produces “vermicompost”, an enriched biofertilizer known to improve the physical, chemical, and biological properties of soil. It is applied in granular form and/or in liquid solution (vermiwash), and in both open fields and greenhouses. Vermicompost [...] Read more.

Incorporation of vermin culture in the composting system produces “vermicompost”, an enriched biofertilizer known to improve the physical, chemical, and biological properties of soil. It is applied in granular form and/or in liquid solution (vermiwash), and in both open fields and greenhouses. Vermicompost has been shown to contain plant growth hormones, which stimulate seed germination and improve crop yield, the ‘marketability’ of products, plant physiology, and their ability to fight against disease. In recent years, South Pacific island countries (SPICs) have placed an increasing emphasis on the importance of organic agricultural practices as a means of achieving more sustainable and environmentally friendly farming practices. However, vermiculture is not practiced in South Pacific island countries (SPICs) largely due to the lack of awareness of this type of application. We consider the inclusion of vermiculture in this region as a potential means of achieving sustainable organic agricultural practices. This study represents a systematic review in which we collect relevant information on vermicomposting and analyze the applicability of this practice in the SPICs based on these nations’ physical, socioeconomic, and climatic conditions. The tropical climate of the SPICs means that they meet the combined requirements of a large available biomass for composting and the availability of earthworms. Perionyx excavatus and Pontoscolex corethrurus have been identified as potential native earthworm species for vermicomposting under the conditions of the SPICs. Eisenia fetida, a well-known earthworm species, is also effectively adapted to this region and reported to be an efficient species for commercial vermicomposting. However, as a new input into the local production system, there may be unforeseen barriers in the initial stages, as with other advanced technologies, and the introduction of vermiculture as a practice requires a steady effort and adaptive research to achieve success. Further experimental research is required to analyze the productivity and profitability of using the identified native earthworm species for vermiculture using locally available biomass in the SPICs. Full article

(This article belongs to the Special Issue Effects of Biochar and Compost Amendments on Soil Fertility)

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18 pages, 4297 KiB

Open AccessEditor’s ChoiceArticle

Potato Surface Defect Detection Based on Deep Transfer Learning

by Chenglong Wang and Zhifeng Xiao

Agriculture 2021, 11(9), 863; https://doi.org/10.3390/agriculture11090863 - 10 Sep 2021

Cited by 24 | Viewed by 4866

Abstract

Food defect detection is crucial for the automation of food production and processing. Potato surface defect detection remains challenging due to the irregular shape of potato individuals and various types of defects. This paper employs deep convolutional neural network (DCNN) models for potato [...] Read more.

Food defect detection is crucial for the automation of food production and processing. Potato surface defect detection remains challenging due to the irregular shape of potato individuals and various types of defects. This paper employs deep convolutional neural network (DCNN) models for potato surface defect detection. In particular, we applied transfer learning by fine-tuning a base model through three DCNN models—SSD Inception V2, RFCN ResNet101, and Faster RCNN ResNet101—on a self-developed dataset, and achieved an accuracy of 92.5%, 95.6%, and 98.7%, respectively. RFCN ResNet101 presented the best overall performance in detection speed and accuracy. It was selected as the final model for out-of-sample testing, further demonstrating the model’s ability to generalize. Full article

(This article belongs to the Special Issue Image Analysis Techniques in Agriculture)

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17 pages, 3874 KiB

Open AccessEditor’s ChoiceArticle

Assessment of Meteorological and Agricultural Drought Occurrence in Central Poland in 1961–2020 as an Element of the Climatic Risk to Crop Production

by Renata Kuśmierek-Tomaszewska and Jacek Żarski

Agriculture 2021, 11(9), 855; https://doi.org/10.3390/agriculture11090855 - 7 Sep 2021

Cited by 13 | Viewed by 3054

Abstract

The results of numerous studies concerning meteorological drought show that there is a considerable impact of this phenomenon on several regions in Europe. On the other hand, statistical trends of dry spell occurrences in some areas of the continent are unclear or even [...] Read more.

The results of numerous studies concerning meteorological drought show that there is a considerable impact of this phenomenon on several regions in Europe. On the other hand, statistical trends of dry spell occurrences in some areas of the continent are unclear or even negative. Therefore, further research should be directed towards a better understanding of this hazard, particularly the seasonal changes, in order to elaborate adequate strategies to prevent and mitigate its undesirable effects. The main goal of the work, conducted as part of the research strategy on contemporary climate change, was to confirm the hypothesis of increasing frequency and intensity of droughts during the period of active plant growth and development (May–August) in central Poland in 1961–2020. The prevailing rainfall conditions in this period determine the production and economic effects of agricultural output. The analysis covered a multiannual period, including two separate climate normals: 1961–1990 and 1991–2020. The work is also aimed at detecting relationships between indicators characterizing meteorological drought (the Standardized Precipitation Index—SPI) and agricultural drought (the actual precipitation deficiency—P_Adef). It was found that the frequency of meteorological droughts in the studied period amounts to 30.0% (severe and extreme constitute 6.7%). No significant increase in the frequency and intensity of meteorological droughts over time was observed. Relationships between meteorological and agricultural drought indicators were significant, so the SPI can be considered an indicator of plant irrigation needs in the studied area. Full article

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

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26 pages, 6216 KiB

Open AccessEditor’s ChoiceArticle

Full-Season Cover Crops and Their Traits That Promote Agroecosystem Services

by Cameron Wagg, Aafke van Erk, Erica Fava, Louis-Pierre Comeau, T. Fatima Mitterboeck, Claudia Goyer, Sheng Li, Andrew McKenzie-Gopsill and Aaron Mills

Agriculture 2021, 11(9), 830; https://doi.org/10.3390/agriculture11090830 - 30 Aug 2021

Cited by 18 | Viewed by 4466

Abstract

Non-marketable crops are increasingly being used as a tool to promote agroecosystem services and sustainable agriculture. Nevertheless, crops vary greatly in the traits by which they capture resources and influence the local ecosystem. Here we report on the traits and associated soil microbial [...] Read more.

Non-marketable crops are increasingly being used as a tool to promote agroecosystem services and sustainable agriculture. Nevertheless, crops vary greatly in the traits by which they capture resources and influence the local ecosystem. Here we report on the traits and associated soil microbial communities that relate to aboveground biomass production, nutrient capture, weed suppression, erosion control and building particulate organic matter of 22 different full-season cover crops. All agroecosystem services were positively correlated with maximum canopy height and leaf area. Rooting density was positively associated with indices of bacterial diversity. While some legumes produced the greatest standing N and P in aboveground biomass, they were also poor at capturing soil nitrate and promoted high levels of potential plant fungal pathogens. Conversely, Brassicaceae crops had the lowest levels of potential plant fungal pathogens, but also suppressed saprophytic fungi and rhizobia. Thus, not all crops are equal in their ability to promote all agroecosystem services, and while some crops may be ideal for promoting a specific agroecosystem service, this could result in a trade-off with another. Nonetheless, our study demonstrates that plant functional traits are informative for the selection of crops for promoting agroecosystem services. Full article

(This article belongs to the Special Issue Plant–Soil Interactions and Agroecosystem Functioning)

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16 pages, 6605 KiB

Open AccessEditor’s ChoiceArticle

Impacts of Background Removal on Convolutional Neural Networks for Plant Disease Classification In-Situ

by Kamal KC, Zhendong Yin, Dasen Li and Zhilu Wu

Agriculture 2021, 11(9), 827; https://doi.org/10.3390/agriculture11090827 - 30 Aug 2021

Cited by 22 | Viewed by 6205

Abstract

Convolutional neural networks have an immense impact on computer vision tasks. However, the accuracy of convolutional neural networks on a dataset is tremendously affected when images within the dataset highly vary. Test images of plant leaves are usually taken in situ. These images, [...] Read more.

Convolutional neural networks have an immense impact on computer vision tasks. However, the accuracy of convolutional neural networks on a dataset is tremendously affected when images within the dataset highly vary. Test images of plant leaves are usually taken in situ. These images, apart from the region of interest, contain unwanted parts of plants, soil, rocks, and/or human body parts. Segmentation helps isolate the target region and a deep convolutional neural network classifies images precisely. Therefore, we combined edge and morphological based segmentation, background subtraction, and the convolutional neural network to help improve accuracy on image sets with images containing clean and cluttered backgrounds. In the proposed system, segmentation was applied to first extract leaf images in the foreground. Several images contained a leaf of interest interposed between unfavorable foregrounds and backgrounds. Background subtraction was implemented to remove the foreground image followed by segmentation to obtain the region of interest. Finally, the images were classified by a pre-trained classification network. The experimental results on two, four, and eight classes of datasets show that the proposed method achieves 98.7%, 96.7%, and 93.57% accuracy by fine-tuned DenseNet121, InceptionV3, and DenseNet121 models, respectively, on a clean dataset. For two class datasets, the accuracy obtained was about 12% higher for a dataset with images taken in the homogeneous background compared to that of a dataset with testing images with a cluttered background. Results also suggest that image sets with clean backgrounds tend to start training with higher accuracy and converge faster. Full article

(This article belongs to the Special Issue Latest Advances for Smart and Sustainable Agriculture)

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15 pages, 490 KiB

Open AccessEditor’s ChoicePerspective

Ammonia Volatilization from Fertilizer Urea—A New Challenge for Agriculture and Industry in View of Growing Global Demand for Food and Energy Crops

by Maria Skorupka and Artur Nosalewicz

Agriculture 2021, 11(9), 822; https://doi.org/10.3390/agriculture11090822 - 29 Aug 2021

Cited by 40 | Viewed by 10564

Abstract

The growing world population and the necessity to meet its nutritional needs despite the limited area of agricultural land pose a serious challenge for agriculture. Agriculture is responsible for 80–95% of total ammonia emissions to the atmosphere, but at the same time it [...] Read more.

The growing world population and the necessity to meet its nutritional needs despite the limited area of agricultural land pose a serious challenge for agriculture. Agriculture is responsible for 80–95% of total ammonia emissions to the atmosphere, but at the same time it has great potential to reduce them. Fertilisation with mineral nitrogen (in particular urea) is responsible for 19.0–20.3% of total ammonia emissions emitted from agriculture. Ammonia emissions have a negative impact on the environment and human health, therefore it is important to minimize the volatilization of ammonia and increase fertiliser efficiency. This is important due to the need to mitigate the negative impact of anthropopressure on the environment in terms of air pollution, negative effect on soils and waters. The application of urease inhibitors during fertilisation with nitrogen fertilisers is one method to reduce ammonia emissions from plant production. Another option to achieve this goal is to reverse the global trend toward maximizing the production of energy crops (intensive fertilisation inevitably increasing ammonia emissions to the environment) for the production of biofuels, which is growing rapidly, taking up arable land that could be used for food production. The aim of the review is to identify the impact of recently introduced technologies for reducing ammonia emissions from urea on agricultural productivity, environment, and crops. It is of importance to reconsider optimization of crop production in arable land, possible owing to the progress in the production, modification, and application of mineral fertilisers and changes in crop structure. A broad debate is necessary with policymakers and stakeholders to define new targets allowing introduction of technologies for conversion of energy crops into energy with a minimal impact on food production and environmental issue. Full article

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

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19 pages, 4289 KiB

Open AccessEditor’s ChoiceArticle

Organic Food Needs More Land and Direct Energy to Be Produced Compared to Food from Conventional Farming: Empirical Evidence from the Czech Republic

by Radka Redlichová, Gabriela Chmelíková, Ivana Blažková, Eliška Svobodová and Inez Naaki Vanderpuje

Agriculture 2021, 11(9), 813; https://doi.org/10.3390/agriculture11090813 - 27 Aug 2021

Cited by 17 | Viewed by 5550

Abstract

This study investigated direct energy consumption and land performance under two different methods of farming—organic and conventional. The aim of our study was to examine the performance of farmers in the Czech Republic and identify the differences between organic and conventional farming regarding [...] Read more.

This study investigated direct energy consumption and land performance under two different methods of farming—organic and conventional. The aim of our study was to examine the performance of farmers in the Czech Republic and identify the differences between organic and conventional farming regarding food safety and direct energy consumption. Based on the data from the Farm Accountancy Data Network of the EU, we measured the performance of both organic and conventional farmers in terms of product per unit of land and direct energy consumption per unit of product regarding the natural condition of the farm localization. Our findings show that organic farms produce lower output with less direct energy per unit of land; however, they need more direct energy for one unit of production. We found that a product from organic agriculture consumes 1.7-fold greater direct energy than a conventional product. The worse the natural conditions for farming, the broader the difference between organic and conventional regimes regarding their performance and energy consumption. Our conclusions may help shape agricultural policy in the Czech Republic, where organic farming is receiving systematic political support, leading to an increase in the proportion of organically farmed arable land. Full article

(This article belongs to the Special Issue Agriculture and Food Systems – Global and Local Comparisons)

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22 pages, 4858 KiB

Open AccessEditor’s ChoiceArticle

Yield Components Stability Assessment of Peas in Conventional and Low-Input Cultivation Systems

by Vasileios Greveniotis, Elisavet Bouloumpasi, Stylianos Zotis, Athanasios Korkovelos and Constantinos G. Ipsilandis

Agriculture 2021, 11(9), 805; https://doi.org/10.3390/agriculture11090805 - 24 Aug 2021

Cited by 14 | Viewed by 2906

Abstract

The primary purpose of this study was to explore yield stability of pea (Pisum sativum L.) cultivars based on stability index, with specific aim at studying cultivar behavior regarding yield of peas under both conventional and low-input cultivation systems. Five cultivars of [...] Read more.

The primary purpose of this study was to explore yield stability of pea (Pisum sativum L.) cultivars based on stability index, with specific aim at studying cultivar behavior regarding yield of peas under both conventional and low-input cultivation systems. Five cultivars of peas were used in a strip-plot design. Correlations showed a significant positive relation between seed yield and some other traits. Indirect seed yield improvement may be implemented by improving pod length, which generally showed high stability indices in Greek mega-environment. Comparisons between conventional and low-input farming systems generally did not affect stability estimations, but revealed cultivars that exhibited stable performance, even in low-input farming systems. The additive main effects and multiplicative interaction (AMMI) biplot analysis, genotype by environment interaction (GGE) biplot analysis and analysis of variance (ANOVA) showed statistically significant differences between genotypes and environments, and also the farming system. This way, we have certain cultivars of peas to recommend for specific areas and farming system, in order to achieve the most stable performance. Vermio proved to be a stable cultivar for seed yield performance, in Giannitsa, Trikala and Kalambaka area, in low-inputs farming systems, while Olympos was the best in Florina area and low-input farming. Full article

(This article belongs to the Special Issue Crop Breeding and Genetics)

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25 pages, 11705 KiB

Open AccessEditor’s ChoiceArticle

Deep-Learning Temporal Predictor via Bidirectional Self-Attentive Encoder–Decoder Framework for IOT-Based Environmental Sensing in Intelligent Greenhouse

by Xue-Bo Jin, Wei-Zhen Zheng, Jian-Lei Kong, Xiao-Yi Wang, Min Zuo, Qing-Chuan Zhang and Seng Lin

Agriculture 2021, 11(8), 802; https://doi.org/10.3390/agriculture11080802 - 23 Aug 2021

Cited by 74 | Viewed by 5384

Abstract

Smart agricultural greenhouses provide well-controlled conditions for crop cultivation but require accurate prediction of environmental factors to ensure ideal crop growth and management efficiency. Due to the limitations of existing predictors in dealing with massive, nonlinear, and dynamic temporal data, this study proposes [...] Read more.

Smart agricultural greenhouses provide well-controlled conditions for crop cultivation but require accurate prediction of environmental factors to ensure ideal crop growth and management efficiency. Due to the limitations of existing predictors in dealing with massive, nonlinear, and dynamic temporal data, this study proposes a bidirectional self-attentive encoder–decoder framework (BEDA) to construct the long-time predictor for multiple environmental factors with high nonlinearity and noise in a smart greenhouse. Firstly, the original data are denoised by wavelet threshold filter and pretreatment operations. Secondly, the bidirectional long short-term-memory is selected as the fundamental unit to extract time-serial features. Then, the multi-head self-attention mechanism is incorporated into the encoder–decoder framework to improve the prediction performance. Experimental investigations are conducted in a practical greenhouse to accurately predict indoor environmental factors (temperature, humidity, and CO₂) from noisy IoT-based sensors. The best model for all datasets was the proposed BEDA method, with the root mean square error of three factors’ prediction reduced to 2.726, 3.621, and 49.817, and with an R of 0.749 for temperature, 0.848 for humidity, and 0.8711 for CO₂ concentration, respectively. The experimental results show that the favorable prediction accuracy, robustness, and generalization of the proposed method make it suitable to more precisely manage greenhouses. Full article

(This article belongs to the Special Issue Future Development Trends of Intelligent Greenhouses)

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13 pages, 1154 KiB

Open AccessEditor’s ChoiceArticle

Global Wheat Market Dynamics: What Is the Role of the EU and the Black Sea Wheat Exporters?

by Miranda Svanidze and Ivan Đurić

Agriculture 2021, 11(8), 799; https://doi.org/10.3390/agriculture11080799 - 22 Aug 2021

Cited by 16 | Viewed by 6268

Abstract

Over the last two decades, three countries in the Black Sea Region—Russia, Ukraine, and Kazakhstan—became global leaders in grain production and trade, and replaced the USA and France as the most previous largest wheat exporting countries. In this study we investigate world wheat [...] Read more.

Over the last two decades, three countries in the Black Sea Region—Russia, Ukraine, and Kazakhstan—became global leaders in grain production and trade, and replaced the USA and France as the most previous largest wheat exporting countries. In this study we investigate world wheat price linkages and identify the current “price leaders” of the global wheat market. This empirical analysis is focused on the price relationships between eight of the largest wheat exporting countries and uses a cointegration framework and a vector error-correction model. The results show that, regarding price formation on the world wheat market, the French price is more important for transmitting price signals to other wheat export markets compared to the USA. Furthermore, our results indicate that, despite being leaders in wheat export volumes, the Black Sea wheat prices in Russia and Ukraine adjust to price changes in France, the USA, and Canada. Albeit unrealistic in the short run, the creation of the futures market in the Black Sea region might significantly improve the participation of Black Sea markets in price formation of the global wheat market. Full article

(This article belongs to the Special Issue Dynamics of Food Value Chains: Resilience, Fairness and Sustainability)

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13 pages, 3012 KiB

Open AccessEditor’s ChoiceArticle

Physiological Traits of Thirty-Five Tomato Accessions in Response to Low Temperature

by Sherzod Nigmatullayevich Rajametov, Kwanuk Lee, Hyo-Bong Jeong, Myeong-Cheoul Cho, Chun-Woo Nam and Eun-Young Yang

Agriculture 2021, 11(8), 792; https://doi.org/10.3390/agriculture11080792 - 19 Aug 2021

Cited by 12 | Viewed by 4381

Abstract

Tomato is often exposed to diverse abiotic stresses and cold stress is one of harsh environmental stresses. Abnormal low temperature affects tomato growth and development, leading to, e.g., physiological disorders, flower drops, and abnormal fruit morphology, and causing a decrease in tomato yield [...] Read more.

Tomato is often exposed to diverse abiotic stresses and cold stress is one of harsh environmental stresses. Abnormal low temperature affects tomato growth and development, leading to, e.g., physiological disorders, flower drops, and abnormal fruit morphology, and causing a decrease in tomato yield and fruit quality. It is important to identify low temperature-(LT) tolerant tomato (Solanum lycopersicum L.) cultivars relying on different fruit types. In this study, our focus was to analyze the physiological traits of 35 tomato accessions with three different fruit types (cherry, medium, and large sizes) under night temperature set-points of 15 °C for control temperature (CT) and 10 °C for LT, respectively. Plant heights (PH) of most tomato accessions in LT were remarkably decreased compared to those in CT. The leaf length (LL) and leaf width (LW) were reduced depending on the genotypes under LT. In addition, the number of fruits (NFR), fruit set (FS), fruit yield (FY), and marketable yield (MY) were negatively affected in LT. The variation was further investigated by the correlation, the principal component (PCA), and the cluster analysis. Interestingly, positive correlations between different vegetative and reproductive traits were uncovered. Multivariate analysis including the PCA and hierarchical clustering classified the LT-treated 35 tomato accessions into four major groups. The identified accessions were associated with vegetative and reproductive parameters on positive directions. The results might be utilized for establishing breeding programs on selecting LT-tolerant tomato cultivars with different selection indices relying on fruit types during vegetative and/or reproductive stages. Full article

(This article belongs to the Special Issue Vegetable Crops Breeding for Abiotic Stress Tolerance and Quality Traits)

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