Agriculture

15 pages, 3483 KiB

Open AccessEditor’s ChoiceArticle

Tea Sprout Picking Point Identification Based on Improved DeepLabV3+

by Chunyu Yan, Zhonghui Chen, Zhilin Li, Ruixin Liu, Yuxin Li, Hui Xiao, Ping Lu and Benliang Xie

Agriculture 2022, 12(10), 1594; https://doi.org/10.3390/agriculture12101594 - 02 Oct 2022

Cited by 14 | Viewed by 2262

Abstract

Tea sprout segmentation and picking point localization via machine vision are the core technologies of automatic tea picking. This study proposes a method of tea segmentation and picking point location based on a lightweight convolutional neural network named MC-DM (Multi-Class DeepLabV3+ MobileNetV2 (Mobile [...] Read more.

Tea sprout segmentation and picking point localization via machine vision are the core technologies of automatic tea picking. This study proposes a method of tea segmentation and picking point location based on a lightweight convolutional neural network named MC-DM (Multi-Class DeepLabV3+ MobileNetV2 (Mobile Networks Vision 2)) to solve the problem of tea shoot picking point in a natural environment. In the MC-DM architecture, an optimized MobileNetV2 is used to reduce the number of parameters and calculations. Then, the densely connected atrous spatial pyramid pooling module is introduced into the MC-DM to obtain denser pixel sampling and a larger receptive field. Finally, an image dataset of high-quality tea sprout picking points is established to train and test the MC-DM network. Experimental results show that the MIoU of MC-DM reached 91.85%, which is improved by 8.35% compared with those of several state-of-the-art methods. The optimal improvements of model parameters and detection speed were 89.19% and 16.05 f/s, respectively. After the segmentation results of the MC-DM were applied to the picking point identification, the accuracy of picking point identification reached 82.52%, 90.07%, and 84.78% for single bud, one bud with one leaf, and one bud with two leaves, respectively. This research provides a theoretical reference for fast segmentation and visual localization of automatically picked tea sprouts. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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21 pages, 1562 KiB

Open AccessEditor’s ChoiceReview

Biochar a Promising Strategy for Pesticide-Contaminated Soils

by Irina Gabriela Cara, Denis Țopa, Ioan Puiu and Gerard Jităreanu

Agriculture 2022, 12(10), 1579; https://doi.org/10.3390/agriculture12101579 - 30 Sep 2022

Cited by 25 | Viewed by 4932

Abstract

Soil pesticide contamination induced by modern agriculture has become a serious global issue. Its uncontrolled and inefficient application is among the main reasons for their enrichment in plants and animals subsequently transferred to humans and providing a public health risk. Biochar as a [...] Read more.

Soil pesticide contamination induced by modern agriculture has become a serious global issue. Its uncontrolled and inefficient application is among the main reasons for their enrichment in plants and animals subsequently transferred to humans and providing a public health risk. Biochar as a renewable and economical carbonaceous material provides a natural solution for immobilizing pesticides and improving soil health. The biochar impact in agricultural contaminated soil is governed by various factors such as the physico-chemical properties of biochar, pyrolysis, soil conditions, and the application method, which can lead to significant gaps in the removal or mitigation of toxic substances. The current study summarizes the negative effects of pesticide use and the advantages of biochar according to other remediation techniques, succeeded by the mechanism and controlling factors on minimizing pesticide leaching and bioavailability in soil. In addition, the role of biochar on fundamental processes of adsorption, desorption, biodegradation, and leaching is discussed. Ultimately, the major future research regulation and key strategies that are fundamental for pesticide-contaminated soil remediation are proposed. Full article

(This article belongs to the Special Issue Contamination and Bioremediation of Agricultural Soils)

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26 pages, 2823 KiB

Open AccessEditor’s ChoiceReview

Sustainable Agro-Food Systems for Addressing Climate Change and Food Security

by Akila Wijerathna-Yapa and Ranjith Pathirana

Agriculture 2022, 12(10), 1554; https://doi.org/10.3390/agriculture12101554 - 26 Sep 2022

Cited by 41 | Viewed by 13748

Abstract

Despite world food production keeping pace with population growth because of the Green Revolution, the United Nations (UN) State of Food Security and Nutrition in the World 2022 Report indicates that the number of people affected by hunger has increased to 828 million [...] Read more.

Despite world food production keeping pace with population growth because of the Green Revolution, the United Nations (UN) State of Food Security and Nutrition in the World 2022 Report indicates that the number of people affected by hunger has increased to 828 million with 29.3% of the global population food insecure, and 22% of children under five years of age stunted. Many more have low-quality, unhealthy diets and micronutrient deficiencies leading to obesity, diabetes, and other diet-related non-communicable diseases. Additionally, current agro-food systems significantly impact the environment and the climate, including soil and water resources. Frequent natural disasters resulting from climate change, pandemics, and conflicts weaken food systems and exacerbate food insecurity worldwide. In this review, we outline the current knowledge in alternative agricultural practices for achieving sustainability as well as policies and practices that need to be implemented for an equitable distribution of resources and food for achieving several goals in the UN 2030 Agenda for Sustainable Development. According to the UN Intergovernmental Panel on Climate Change, animal husbandry, particularly ruminant meat and dairy, accounts for a significant proportion of agricultural greenhouse gas (GHG) emissions and land use but contributes only 18% of food energy. In contrast, plant-based foods, particularly perennial crops, have the lowest environmental impacts. Therefore, expanding the cultivation of perennials, particularly herbaceous perennials, to replace annual crops, fostering climate-smart food choices, implementing policies and subsidies favoring efficient production systems with low environmental impact, empowering women, and adopting modern biotechnological and digital solutions can help to transform global agro-food systems toward sustainability. There is growing evidence that food security and adequate nutrition for the global population can be achieved using climate-smart, sustainable agricultural practices, while reducing negative environmental impacts of agriculture, including GHG emissions. Full article

(This article belongs to the Special Issue Green and Sustainable Agricultural Ecosystem)

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15 pages, 3815 KiB

Open AccessEditor’s ChoiceArticle

Weed Detection in Peanut Fields Based on Machine Vision

by Hui Zhang, Zhi Wang, Yufeng Guo, Ye Ma, Wenkai Cao, Dexin Chen, Shangbin Yang and Rui Gao

Agriculture 2022, 12(10), 1541; https://doi.org/10.3390/agriculture12101541 - 24 Sep 2022

Cited by 17 | Viewed by 3251

Abstract

The accurate identification of weeds in peanut fields can significantly reduce the use of herbicides in the weed control process. To address the identification difficulties caused by the cross-growth of peanuts and weeds and by the variety of weed species, this paper proposes [...] Read more.

The accurate identification of weeds in peanut fields can significantly reduce the use of herbicides in the weed control process. To address the identification difficulties caused by the cross-growth of peanuts and weeds and by the variety of weed species, this paper proposes a weed identification model named EM-YOLOv4-Tiny incorporating multiscale detection and attention mechanisms based on YOLOv4-Tiny. Firstly, an Efficient Channel Attention (ECA) module is added to the Feature Pyramid Network (FPN) of YOLOv4-Tiny to improve the recognition of small target weeds by using the detailed information of shallow features. Secondly, the soft Non-Maximum Suppression (soft-NMS) is used in the output prediction layer to filter the best prediction frames to avoid the problem of missed weed detection caused by overlapping anchor frames. Finally, the Complete Intersection over Union (CIoU) loss is used to replace the original Intersection over Union (IoU) loss so that the model can reach the convergence state faster. The experimental results show that the EM-YOLOv4-Tiny network is 28.7 M in size and takes 10.4 ms to detect a single image, which meets the requirement of real-time weed detection. Meanwhile, the mAP on the test dataset reached 94.54%, which is 6.83%, 4.78%, 6.76%, 4.84%, and 9.64% higher compared with YOLOv4-Tiny, YOLOv4, YOLOv5s, Swin-Transformer, and Faster-RCNN, respectively. The method has much reference value for solving the problem of fast and accurate weed identification in peanut fields. Full article

(This article belongs to the Special Issue Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring)

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18 pages, 7315 KiB

Open AccessEditor’s ChoiceArticle

Automatic Tandem Dual BlendMask Networks for Severity Assessment of Wheat Fusarium Head Blight

by Yichao Gao, Hetong Wang, Man Li and Wen-Hao Su

Agriculture 2022, 12(9), 1493; https://doi.org/10.3390/agriculture12091493 - 18 Sep 2022

Cited by 14 | Viewed by 2553

Abstract

Fusarium head blight (FHB) disease reduces wheat yield and quality. Breeding wheat varieties with resistance genes is an effective way to reduce the impact of this disease. This requires trained experts to assess the disease resistance of hundreds of wheat lines in the [...] Read more.

Fusarium head blight (FHB) disease reduces wheat yield and quality. Breeding wheat varieties with resistance genes is an effective way to reduce the impact of this disease. This requires trained experts to assess the disease resistance of hundreds of wheat lines in the field. Manual evaluation methods are time-consuming and labor-intensive. The evaluation results are greatly affected by human factors. Traditional machine learning methods are only suitable for small-scale datasets. Intelligent and accurate assessment of FHB severity could significantly facilitate rapid screening of resistant lines. In this study, the automatic tandem dual BlendMask deep learning framework was used to simultaneously segment the wheat spikes and diseased areas to enable the rapid detection of the disease severity. The feature pyramid network (FPN), based on the ResNet-50 network, was used as the backbone of BlendMask for feature extraction. The model exhibited positive performance in the segmentation of wheat spikes with precision, recall, and MIoU (mean intersection over union) values of 85.36%, 75.58%, and 56.21%, respectively, and the segmentation of diseased areas with precision, recall, and MIoU values of 78.16%, 79.46%, and 55.34%, respectively. The final recognition accuracies of the model for wheat spikes and diseased areas were 85.56% and 99.32%, respectively. The disease severity was obtained from the ratio of the diseased area to the spike area. The average accuracy for FHB severity classification reached 91.80%, with the average F1-score of 92.22%. This study demonstrated the great advantage of a tandem dual BlendMask network in intelligent screening of resistant wheat lines. Full article

(This article belongs to the Special Issue Computer Vision for Intelligent Crop Identification and Crop Protection)

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21 pages, 1361 KiB

Open AccessEditor’s ChoiceArticle

Has China’s Carbon Emissions Trading Pilot Policy Improved Agricultural Green Total Factor Productivity?

by Zhuohui Yu, Shiping Mao and Qingning Lin

Agriculture 2022, 12(9), 1444; https://doi.org/10.3390/agriculture12091444 - 12 Sep 2022

Cited by 16 | Viewed by 2292

Abstract

The carbon trading system affects all aspects of the economy and society profoundly. Agriculture, as a high-carbon-emitting industry, has been hard-hit. China’s agricultural activities will emit about 820 million tons of carbon dioxide equivalents, accounting for 7% of the country’s total carbon emissions. [...] Read more.

The carbon trading system affects all aspects of the economy and society profoundly. Agriculture, as a high-carbon-emitting industry, has been hard-hit. China’s agricultural activities will emit about 820 million tons of carbon dioxide equivalents, accounting for 7% of the country’s total carbon emissions. In order to develop a green and low-carbon economy and control greenhouse gas emissions, China officially launched the pilot carbon emissions trading policy in 2013. The effects and mechanism of this on agricultural carbon emissions are still unclear. Herein, this paper uses China’s provincial panel data from 2000 to 2019 to measure agricultural green total factor productivity regarding the implementation of China’s carbon emissions trading pilot policy in 2013 as a quasi-natural experiment, and uses PSM-DID robustness analysis to evaluate the effect of China’s carbon emission rights trading pilot policy on agricultural green total factor productivity in pilot areas. The propensity score method is a type of statistical method that uses nonexperimental or observational data for intervention-effect analysis, which reduces the effects of bias and allows for more reasonable comparisons between treatment and control groups. “Difference in difference” is an approach to policy-effect evaluation based on a counterfactual framework to assess the change in the observed factors in both cases of policy occurrence and nonoccurrence. PSM-DID is a combination of PSM and DID using the PSM method to match each treatment group sample to a specific control group sample, which can solve the problem of self-selection bias in the DID method and assess the policy implementation effect more accurately. This study found that China’s carbon emissions trading pilot policy has significantly improved China’s agricultural green total factor productivity. Further impact mechanism tests show that China’s carbon emissions trading pilot policy will improve agricultural green total factor productivity through environmental protection policies and technological innovation. Finally, this paper puts forward corresponding countermeasures and suggestions based on the research results. Full article

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

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15 pages, 359 KiB

Open AccessEditor’s ChoiceReview

Yeasts as a Potential Biological Agent in Plant Disease Protection and Yield Improvement—A Short Review

by Jolanta Kowalska, Joanna Krzymińska and Józef Tyburski

Agriculture 2022, 12(9), 1404; https://doi.org/10.3390/agriculture12091404 - 06 Sep 2022

Cited by 26 | Viewed by 6256

Abstract

The role of biocontrol products is expected to increase worldwide consumer demand and facilitate the implementation of sustainable agricultural policies. New biocontrol agents must allow for an effective crop-protection strategy in sustainable agriculture. Yeasts are microorganisms living in various niches of the environment [...] Read more.

The role of biocontrol products is expected to increase worldwide consumer demand and facilitate the implementation of sustainable agricultural policies. New biocontrol agents must allow for an effective crop-protection strategy in sustainable agriculture. Yeasts are microorganisms living in various niches of the environment that can be antagonists of many plant pathogens. Yeasts rapidly colonize plant surfaces, use nutrients from many sources, survive in a relatively wide temperature range, produce no harmful metabolites and have no deleterious effects on the final food products. Hence, they can be a good biocontrol agent. In this paper, the biological characteristics and potential of yeast are summarized. Additionally, the mechanisms of yeasts as plant-protection agents are presented. This includes the production of volatile organic compounds, production of killer toxins, competition for space and nutrient compounds, production of lytic enzymes, induction of plant immunity and mycoparasitism. The mechanisms of yeast interaction with plant hosts are also described, and examples of yeasts used for pre- and postharvest biocontrol are provided. Commercially available yeast-based products are listed and challenges for yeast-based products are described. Full article

(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)

21 pages, 1979 KiB

Open AccessEditor’s ChoiceReview

Reducing Carbon Footprint of Agriculture—Can Organic Farming Help to Mitigate Climate Change?

by Małgorzata Holka, Jolanta Kowalska and Magdalena Jakubowska

Agriculture 2022, 12(9), 1383; https://doi.org/10.3390/agriculture12091383 - 03 Sep 2022

Cited by 37 | Viewed by 13675

Abstract

In the face of a changing climate, intensive efforts are needed for limiting the global temperature increase to 1.5 °C. Agricultural production has the potential to play an important role in mitigating climate change. It is necessary to optimize all of the agricultural [...] Read more.

In the face of a changing climate, intensive efforts are needed for limiting the global temperature increase to 1.5 °C. Agricultural production has the potential to play an important role in mitigating climate change. It is necessary to optimize all of the agricultural practices that have high levels of greenhouse gas (GHG) emissions. Among the plant production processes, mineral fertilization is of the greatest importance in the formation of the carbon footprint (CF) of crops. There are many possibilities for reducing GHG emissions from the application of fertilizers. Further benefits in reducing the CF can be obtained through combining tillage treatments, reduced and no-till technologies, and the cultivation of catch crops and leguminous plants. Organic farming has the potential for reducing GHG emissions and improving organic carbon sequestration. This system eliminates synthetic nitrogen fertilizers and thus could lower global agricultural GHG emissions. Organic farming could result in a higher soil organic carbon content compared to non-organic systems. When used together with other environmentally friendly farming practices, significant reductions of GHG emissions can be achieved. Full article

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

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23 pages, 571 KiB

Open AccessEditor’s ChoiceReview

Machine Learning for Detection and Prediction of Crop Diseases and Pests: A Comprehensive Survey

by Tiago Domingues, Tomás Brandão and João C. Ferreira

Agriculture 2022, 12(9), 1350; https://doi.org/10.3390/agriculture12091350 - 01 Sep 2022

Cited by 48 | Viewed by 17592

Abstract

Considering the population growth rate of recent years, a doubling of the current worldwide crop productivity is expected to be needed by 2050. Pests and diseases are a major obstacle to achieving this productivity outcome. Therefore, it is very important to develop efficient [...] Read more.

Considering the population growth rate of recent years, a doubling of the current worldwide crop productivity is expected to be needed by 2050. Pests and diseases are a major obstacle to achieving this productivity outcome. Therefore, it is very important to develop efficient methods for the automatic detection, identification, and prediction of pests and diseases in agricultural crops. To perform such automation, Machine Learning (ML) techniques can be used to derive knowledge and relationships from the data that is being worked on. This paper presents a literature review on ML techniques used in the agricultural sector, focusing on the tasks of classification, detection, and prediction of diseases and pests, with an emphasis on tomato crops. This survey aims to contribute to the development of smart farming and precision agriculture by promoting the development of techniques that will allow farmers to decrease the use of pesticides and chemicals while preserving and improving their crop quality and production. Full article

(This article belongs to the Topic Applications of Big Data and Machine Learning in Smart Agriculture)

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15 pages, 3241 KiB

Open AccessEditor’s ChoiceArticle

Heat Shock Treatment Promoted Callus Formation on Postharvest Sweet Potato by Adjusting Active Oxygen and Phenylpropanoid Metabolism

by Qi Xin, Bangdi Liu, Jing Sun, Xinguang Fan, Xiangxin Li, Lihua Jiang, Guangfei Hao, Haisheng Pei and Xinqun Zhou

Agriculture 2022, 12(9), 1351; https://doi.org/10.3390/agriculture12091351 - 01 Sep 2022

Cited by 12 | Viewed by 1897

Abstract

This study aimed to investigate that rapid high-temperature treatment (RHT) at an appropriate temperature could accelerate callus formation by effectively promoting the necessary metabolic pathways in sweet potato callus. In this study, the callus of sweet potato was treated with heat shock at [...] Read more.

This study aimed to investigate that rapid high-temperature treatment (RHT) at an appropriate temperature could accelerate callus formation by effectively promoting the necessary metabolic pathways in sweet potato callus. In this study, the callus of sweet potato was treated with heat shock at 50, 65, and 80 °C for 15 min. The callus formation was observed within 1, 3, and 5 days, and the accumulation of intermediates in the metabolism of phenylpropane and reactive oxygen species and changes in enzyme activities were determined. The results showed that appropriate RHT treatment at 65 °C stimulated the metabolism of reactive oxygen species at the injury site of sweet potato on the first day, and maintained a high level of reactive oxygen species production and scavenging within 5 days. The higher level of reactive oxygen species stimulated the phenylalanine ammonia-lyase (PAL), 4-coumarate-CoA ligase and cinnamate-4-hydroxylase activities of the phenylpropane metabolic pathway, and promoted the rapid synthesis of chlorogenic acid, p-coumaric acid, rutin, and caffeic acid at the injury site, which stacked to form callus. By Pearson’s correlation analysis, catalase (CAT), PAL, and chlorogenic acid content were found to be strongly positively correlated with changes in all metabolites and enzymatic activities. Our results indicated that appropriate high-temperature rapid treatment could promote sweet potato callus by inducing reactive oxygen species and phenylpropane metabolism; moreover, CAT, PAL, and chlorogenic acid were key factors in promoting two metabolic pathways in sweet potato callus. Full article

(This article belongs to the Special Issue Abiotic Stresses, Biostimulants and Plant Activity)

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19 pages, 13641 KiB

Open AccessEditor’s ChoiceArticle

Detection Method of Cow Estrus Behavior in Natural Scenes Based on Improved YOLOv5

by Rong Wang, Zongzhi Gao, Qifeng Li, Chunjiang Zhao, Ronghua Gao, Hongming Zhang, Shuqin Li and Lu Feng

Agriculture 2022, 12(9), 1339; https://doi.org/10.3390/agriculture12091339 - 30 Aug 2022

Cited by 19 | Viewed by 2783

Abstract

Natural breeding scenes have the characteristics of a large number of cows, complex lighting, and a complex background environment, which presents great difficulties for the detection of dairy cow estrus behavior. However, the existing research on cow estrus behavior detection works well in [...] Read more.

Natural breeding scenes have the characteristics of a large number of cows, complex lighting, and a complex background environment, which presents great difficulties for the detection of dairy cow estrus behavior. However, the existing research on cow estrus behavior detection works well in ideal environments with a small number of cows and has a low inference speed and accuracy in natural scenes. To improve the inference speed and accuracy of cow estrus behavior in natural scenes, this paper proposes a cow estrus behavior detection method based on the improved YOLOv5. By improving the YOLOv5 model, it has stronger detection ability for complex environments and multi-scale objects. First, the atrous spatial pyramid pooling (ASPP) module is employed to optimize the YOLOv5l network at multiple scales, which improves the model’s receptive field and ability to perceive global contextual multiscale information. Second, a cow estrus behavior detection model is constructed by combining the channel-attention mechanism and a deep-asymmetric-bottleneck module. Last, K-means clustering is performed to obtain new anchors and complete intersection over union (CIoU) is used to introduce the relative ratio between the predicted box of the cow mounting and the true box of the cow mounting to the regression box prediction function to improve the scale invariance of the model. Multiple cameras were installed in a natural breeding scene containing 200 cows to capture videos of cows mounting. A total of 2668 images were obtained from 115 videos of cow mounting events from the training set, and 675 images were obtained from 29 videos of cow mounting events from the test set. The training set is augmented by the mosaic method to increase the diversity of the dataset. The experimental results show that the average accuracy of the improved model was 94.3%, that the precision was 97.0%, and that the recall was 89.5%, which were higher than those of mainstream models such as YOLOv5, YOLOv3, and Faster R-CNN. The results of the ablation experiments show that ASPP, new anchors, C3SAB, and C3DAB designed in this study can improve the accuracy of the model by 5.9%. Furthermore, when the ASPP dilated convolution was set to (1,5,9,13) and the loss function was set to CIoU, the model had the highest accuracy. The class activation map function was utilized to visualize the model’s feature extraction results and to explain the model’s region of interest for cow images in natural scenes, which demonstrates the effectiveness of the model. Therefore, the model proposed in this study can improve the accuracy of the model for detecting cow estrus events. Additionally, the model’s inference speed was 71 frames per second (fps), which meets the requirements of fast and accurate detection of cow estrus events in natural scenes and all-weather conditions. Full article

(This article belongs to the Special Issue Recent Advancements in Precision Livestock Farming)

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38 pages, 2250 KiB

Open AccessEditor’s ChoiceReview

Survey on the Applications of Blockchain in Agriculture

by Krithika L.B.

Agriculture 2022, 12(9), 1333; https://doi.org/10.3390/agriculture12091333 - 29 Aug 2022

Cited by 25 | Viewed by 7631

Abstract

Dating back many millennia, agriculture is an ancient practice in the evolution of civilization. It was developed when humans thought about it and concluded that not everyone in the community was required to produce food. Instead, specialized labor, tools, and techniques could help [...] Read more.

Dating back many millennia, agriculture is an ancient practice in the evolution of civilization. It was developed when humans thought about it and concluded that not everyone in the community was required to produce food. Instead, specialized labor, tools, and techniques could help people achieve surplus food for their community. Since then, agriculture has continuously evolved across the ages and has occupied a vital, synergistic position in the existence of humanity. The evolution of agriculture was based on a compulsion to feed the growing population, and, importantly, maintain the quality and traceability of food, prevent counterfeit products, and modernize and optimize yield. Recent trends and advancements in blockchain technology have some significant attributes that are ideal for agriculture. The invention and implementation of blockchain have caused a fair share of positive disruptions and evolutionary adoption in agriculture to modernize the domain. Blockchain has been adopted at various stages of the agriculture lifecycle for improved evolution. This work presents an intense survey of the literature on how blockchain has positively impacted and continues to influence various market verticals in agriculture, the challenges and the future. Full article

(This article belongs to the Special Issue Data-Driven Agricultural Innovations with Artificial Intelligence and Industrial Internet of Things (IIoT))

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17 pages, 1521 KiB

Open AccessEditor’s ChoiceArticle

Causal Linkage among Agricultural Insurance, Air Pollution, and Agricultural Green Total Factor Productivity in United States: Pairwise Granger Causality Approach

by Nihal Ahmed, Zeeshan Hamid, Farhan Mahboob, Khalil Ur Rehman, Muhammad Sibt e Ali, Piotr Senkus, Aneta Wysokińska-Senkus, Paweł Siemiński and Adam Skrzypek

Agriculture 2022, 12(9), 1320; https://doi.org/10.3390/agriculture12091320 - 26 Aug 2022

Cited by 31 | Viewed by 3787

Abstract

Agricultural insurance and green agriculture are strongly related. Agricultural insurance not only motivates farmers to adopt environmentally friendly production technology and enhances the effectiveness of production, but it also accomplishes the goal of lowering the number of chemicals that are put into the [...] Read more.

Agricultural insurance and green agriculture are strongly related. Agricultural insurance not only motivates farmers to adopt environmentally friendly production technology and enhances the effectiveness of production, but it also accomplishes the goal of lowering the number of chemicals that are put into the environment. This article investigates the dynamic relationship between agricultural insurance, air pollution, and agricultural green total factor productivity. To complete the aim, the authors used the panel auto-regressive distributed lags method (PMG method) and panel data from 50 states of the United States between 2005 and 2019. The empirical findings demonstrate a considerable co-integration and a cross-sectional reliance between agricultural insurance, air pollution, and agricultural green total factor production. Expanding agricultural insurance may boost agricultural green whole factor output but also exacerbate air pollution. However, significant air pollution does not increase agricultural production’s green total factor productivity. The panel Granger causality test shows a one-way causal relationship between agricultural insurance, green total factor productivity, and air pollution. A one-way causal relationship exists between air pollution and agricultural green total factor productivity. The author concluded that improving agricultural insurance coverage or cutting down on air pollution will boost agricultural green total factor output. These findings have long-term policy and management repercussions, particularly for those involved in agriculture policy and environmental management. Full article

(This article belongs to the Special Issue Agricultural Insurance, Risk Management and Sustainable Development)

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22 pages, 1252 KiB

Open AccessEditor’s ChoiceArticle

Comparative Analysis of Environmental and Economic Performance of Agricultural Cooperatives and Smallholder Farmers for Apple Production in China

by Juanjuan Cheng, Qian Wang, Dongjian Li and Jin Yu

Agriculture 2022, 12(8), 1281; https://doi.org/10.3390/agriculture12081281 - 22 Aug 2022

Cited by 13 | Viewed by 2705

Abstract

China is the world’s largest apple producer, and agricultural cooperatives play an important role in promoting sustainable production in its whole life cycle system. However, few studies on cooperatives have evaluated the environmental and economic performance from the life cycle thinking perspective. In [...] Read more.

China is the world’s largest apple producer, and agricultural cooperatives play an important role in promoting sustainable production in its whole life cycle system. However, few studies on cooperatives have evaluated the environmental and economic performance from the life cycle thinking perspective. In this study, the combined methods of life cycle assessment (LCA) and life cycle cost (LCC) were used to comparatively analyze the environmental and economic performance of apple production between cooperatives and smallholder farmers. The results showed that, compared to the smallholder farmers, cooperatives significantly reduced resource depletion and environmental impacts by 12.50–22.16% in each category. The total environmental index for the cooperatives was 7.44% and 22.09% lower than smallholder farmers; meanwhile, the total LCC was 2659.71 Chinese Yuan (CNY), 19.27% lower than smallholder farmers. However, the net profit was 2990.29 CNY for the cooperatives, 21.23% higher than smallholder farmers. The results indicated that cooperatives exhibited a higher net profit while having lower resource input, environmental impact, and LCC than smallholder farmers. Moreover, pesticides and fertilizers were identified as the most critical environmental hotspots. Moreover, human labor cost was the most significant contributor to the total economic cost of the apple production system. These findings provide insights into optimizing farm inputs for apple production and active participation in agricultural cooperatives to alleviate multiple environmental impacts while maintaining apple yield and improving economic benefits, intending to make a marginal contribution to promoting sustainable development of the apple industry in China. Full article

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

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21 pages, 3397 KiB

Open AccessEditor’s ChoiceArticle

Estimation of Maize LAI Using Ensemble Learning and UAV Multispectral Imagery under Different Water and Fertilizer Treatments

by Qian Cheng, Honggang Xu, Shuaipeng Fei, Zongpeng Li and Zhen Chen

Agriculture 2022, 12(8), 1267; https://doi.org/10.3390/agriculture12081267 - 19 Aug 2022

Cited by 16 | Viewed by 2396

Abstract

The leaf area index (LAI), commonly used as an indicator of crop growth and physiological development, is mainly influenced by the degree of water and fertilizer stress. Accurate assessment of the LAI can help to understand the state of crop water and fertilizer [...] Read more.

The leaf area index (LAI), commonly used as an indicator of crop growth and physiological development, is mainly influenced by the degree of water and fertilizer stress. Accurate assessment of the LAI can help to understand the state of crop water and fertilizer deficit, which is important for crop management and the precision agriculture. The objective of this study is to evaluate the unmanned aerial vehicle (UAV)-based multispectral imaging to estimate the LAI of maize under different water and fertilizer stress conditions. For this, multispectral imagery of the field was conducted at different growth stages (jointing, trumpet, silking and flowering) of maize under three water treatments and five fertilizer treatments. Subsequently, a stacking ensemble learning model was built with Gaussian process regression (GPR), support vector regression (SVR), random forest (RF), least absolute shrinkage and selection operator (Lasso) and cubist regression as primary learners to predict the LAI using UAV-based vegetation indices (VIs) and ground truth data. Results showed that the LAI was influenced significantly by water and fertilizer stress in both years’ experiments. Multispectral VIs were significantly correlated with maize LAI at multiple growth stages. The Pearson correlation coefficients between UAV-based VIs and ground truth LAI ranged from 0.64 to 0.89. Furthermore, the fusion of multiple stage data showed that the correlations were significantly higher between ground truth LAI and UAV-based VIs than that of single growth stage data. The ensemble learning algorithm with MLR as the secondary learner outperformed as a single machine learning algorithm with high prediction accuracy R² = 0.967 and RMSE = 0.198 in 2020, and R² = 0.897 and RMSE = 0.220 in 2021. We believe that the ensemble learning algorithm based on stacking is preferable to the single machine learning algorithm to build the LAI prediction model. This study can provide certain theoretical guidance for the rapid and precise management of water and fertilizer for large experimental fields. Full article

(This article belongs to the Section Digital Agriculture)

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20 pages, 1736 KiB

Open AccessEditor’s ChoiceReview

Soil Inorganic Carbon as a Potential Sink in Carbon Storage in Dryland Soils—A Review

by Anandkumar Naorem, Somasundaram Jayaraman, Ram C. Dalal, Ashok Patra, Cherukumalli Srinivasa Rao and Rattan Lal

Agriculture 2022, 12(8), 1256; https://doi.org/10.3390/agriculture12081256 - 18 Aug 2022

Cited by 20 | Viewed by 4497

Abstract

Soil organic carbon (SOC) pool has been extensively studied in the carbon (C) cycling of terrestrial ecosystems. In dryland regions, however, soil inorganic carbon (SIC) has received increasing attention due to the high accumulation of SIC in arid soils contributed by its high [...] Read more.

Soil organic carbon (SOC) pool has been extensively studied in the carbon (C) cycling of terrestrial ecosystems. In dryland regions, however, soil inorganic carbon (SIC) has received increasing attention due to the high accumulation of SIC in arid soils contributed by its high temperature, low soil moisture, less vegetation, high salinity, and poor microbial activities. SIC storage in dryland soils is a complex process comprising multiple interactions of several factors such as climate, land use types, farm management practices, irrigation, inherent soil properties, soil biotic factors, etc. In addition, soil C studies in deeper layers of drylands have opened-up several study aspects on SIC storage. This review explains the mechanisms of SIC formation in dryland soils and critically discusses the SIC content in arid and semi-arid soils as compared to SOC. It also addresses the complex relationship between SIC and SOC in dryland soils. This review gives an overview of how climate change and anthropogenic management of soil might affect the SIC storage in dryland soils. Dryland soils could be an efficient sink in C sequestration through the formation of secondary carbonates. The review highlights the importance of an in-depth understanding of the C cycle in arid soils and emphasizes that SIC dynamics must be looked into broader perspective vis-à-vis C sequestration and climate change mitigation. Full article

(This article belongs to the Special Issue Soil Organic Matter and Its Role in Soil Fertility)

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12 pages, 3618 KiB

Open AccessEditor’s ChoiceArticle

Grape Cluster Real-Time Detection in Complex Natural Scenes Based on YOLOv5s Deep Learning Network

by Chuandong Zhang, Huali Ding, Qinfeng Shi and Yunfei Wang

Agriculture 2022, 12(8), 1242; https://doi.org/10.3390/agriculture12081242 - 17 Aug 2022

Cited by 19 | Viewed by 3130

Abstract

Due to differences in planting environment, color, shape, size, and compactness, accurate detection of grape clusters is very difficult. Herein, a real-time detection method for grape clusters based on the YOLOv5s deep learning algorithm was proposed. More specifically, a novel dataset called Grape-internet [...] Read more.

Due to differences in planting environment, color, shape, size, and compactness, accurate detection of grape clusters is very difficult. Herein, a real-time detection method for grape clusters based on the YOLOv5s deep learning algorithm was proposed. More specifically, a novel dataset called Grape-internet was constructed, which consisted of 8657 grape images and corresponding annotation files in complex scenes. By training and adjusting the parameters of the YOLOv5s model on the data set, and by reducing the depth and width of the network, the lightweight processing of the network was completed, losing only a small amount of accuracy. As a result, the fast and accurate detection of grape clusters was finally realized. The test results showed that the precision, recall, mAP and F1 of the grape cluster detection network were 99.40%, 99.40%, 99.40% and 99.40%, respectively, and the average detection speed per image was 344.83 fps, with a model size of 13.67 MB. Compared with the YOLOv5x, ScaledYOLOv4-CSP and YOLOv3 models, the precision of YOLOv5s was 1.84% higher than that of ScaledYOLOv4-CSP, and the recall rate and mAP were slightly lower than three networks by 0.1–0.3%. The speed was the fastest (4.6 times, 2.83 times and 6.7 times of YOLOv3, ScaledYOLOv4-CSP and YOLOv5x network, respectively) and the network scale was the smallest (1.61%, 6.81% and 8.28% of YOLOv3, ScaledYOLOv4-CSP YOLOv5x, respectively) for YOLOv5s. Moreover, the detection precision and recall rate of YOLOv5s was 26.14% and 30.96% higher, respectively, than those of Mask R-CNN. Further, it exhibited more lightweight and better real-time performance. In short, the detection network can not only meet the requirements of being a high precision, high speed and lightweight solution for grape cluster detection, but also it can adapt to differences between products and complex environmental interference, possessing strong robustness, generalization, and real-time adaptability. Full article

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

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19 pages, 5376 KiB

Open AccessEditor’s ChoiceArticle

Analysis of the Coupling Characteristics of Water Resources and Food Security: The Case of Northwest China

by Xian Liu, Yueyue Xu, Shikun Sun, Xining Zhao and Yubao Wang

Agriculture 2022, 12(8), 1114; https://doi.org/10.3390/agriculture12081114 - 28 Jul 2022

Cited by 14 | Viewed by 1836

Abstract

Exploring the coupling characteristics of regional water resources and food security helps to promote the sustainable development of grain production and is of great significance for achieving global food security. From the aspects of regional “water supply”, “water use” and “water demand”, the [...] Read more.

Exploring the coupling characteristics of regional water resources and food security helps to promote the sustainable development of grain production and is of great significance for achieving global food security. From the aspects of regional “water supply”, “water use” and “water demand”, the coupling characteristics of water resources and food security were systematically revealed; the new challenges faced by regional food security from the perspective of water resources were clarified; and effective ways to promote the utilization of regional water resources and the sustainable development of grain production were explored. This paper took Northwest China, which is the most arid region, where water-resource utilization and food security are in contradiction, as the research area. The water-resource load index, the water footprint of grain production and the water-consumption footprint were used to quantify the regional water-resource pressure index, as well as the residential grain-consumption types, population urbanization, the industrial-grain-processing industry and their corresponding water-consumption footprints from 2000 to 2020. The coupling characteristics of water resources and food security were systematically revealed. The results showed the following: (1) In 2000–2020, the water-resource load index increased from 4.0 to 10.7, and the load level increased from III to I. At the same time, agricultural water resources were largely allocated elsewhere. (2) During the period, the food rations showed a significant decreasing trend, and the average annual reduction was 3.4% (p < 0.01). The water footprint of animal products increased, particularly for beef and poultry (the average annual growth rates were 9.9% and 6.3%, respectively). In addition, the water footprint of industrial food consumption increased by 297.1%. (3) With the improvement of the urbanization level, the water-consumption footprint increased by 85.9%. It is expected that the water footprint of grain consumption will increase by 39.4% and 52.3% by 2030 and 2040, respectively. Exploring how to take effective measures to reduce the water footprint to meet food-security needs is imperative. This study proposed measures to improve the utilization efficiency of blue and green water and reduce gray water and the grain-consumption water footprint from the aspects of regional planting-structure optimization potential, water-saving irrigation technology, dietary-structure transformation and virtual water trade; these measures could better relieve the water-resource pressure and promote the sustainable development of grain production and water-resource utilization. Full article

(This article belongs to the Special Issue Sustainable Remediation and Management of Agricultural Soil and Water Resources)

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22 pages, 2113 KiB

Open AccessEditor’s ChoiceArticle

The Impact of the Digital Economy on Agricultural Green Development: Evidence from China

by Qi Jiang, Jizhi Li, Hongyun Si and Yangyue Su

Agriculture 2022, 12(8), 1107; https://doi.org/10.3390/agriculture12081107 - 27 Jul 2022

Cited by 28 | Viewed by 4875

Abstract

Whether the digital economy can effectively promote agricultural green development is crucial to the realization of agricultural rural modernization. This study empirically analyzes the impact of the digital economy on agricultural green development and the mechanism of action based on panel data of [...] Read more.

Whether the digital economy can effectively promote agricultural green development is crucial to the realization of agricultural rural modernization. This study empirically analyzes the impact of the digital economy on agricultural green development and the mechanism of action based on panel data of 30 Chinese provinces from 2011 to 2020. The results reveal that (1) the digital economy can significantly improve the green development level of China’s agriculture; the dividends in the eastern region and central region are significantly higher than that in the western region, and there is regional heterogeneity. (2) The role of the digital economy in promoting agricultural green development has a nonlinear characteristic of increasing “marginal effect.” (3) The digital economy has a significant spatial spillover effect, which can have a positive impact on agricultural green development in the surrounding areas. (4) The construction of “Broadband Countryside” can improve the development of the rural digital economy and indirectly promote agricultural green development. This study deepens our understanding of the internal effect and interval relationship of how the digital economy enables agricultural green development and provides the theoretical basis and practical suggestions for optimizing digital facility construction and high-quality agricultural development. 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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16 pages, 10486 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Tea Category Identification Using Wavelet Signal Reconstruction of Hyperspectral Imagery and Machine Learning

by Qiang Cui, Baohua Yang, Biyun Liu, Yunlong Li and Jingming Ning

Agriculture 2022, 12(8), 1085; https://doi.org/10.3390/agriculture12081085 - 23 Jul 2022

Cited by 16 | Viewed by 2360

Abstract

Accurately distinguishing the types of tea is of great significance to the pricing, production, and processing of tea. The similarity of the internal spectral characteristics and appearance characteristics of different types of tea greatly limits further research on tea identification. However, wavelet transform [...] Read more.

Accurately distinguishing the types of tea is of great significance to the pricing, production, and processing of tea. The similarity of the internal spectral characteristics and appearance characteristics of different types of tea greatly limits further research on tea identification. However, wavelet transform can simultaneously extract time domain and frequency domain features, which is a powerful tool in the field of image signal processing. To address this gap, a method for tea recognition based on a lightweight convolutional neural network and support vector machine (L-CNN-SVM) was proposed, aiming to realize tea recognition using wavelet feature figures generated by wavelet time-frequency signal decomposition and reconstruction. Firstly, the redundant discrete wavelet transform was used to decompose the wavelet components of the hyperspectral images of the three teas (black tea, green tea, and yellow tea), which were used to construct the datasets. Secondly, improve the lightweight CNN model to generate a tea recognition model. Finally, compare and evaluate the recognition results of different models. The results demonstrated that the results of tea recognition based on the L-CNN-SVM method outperformed MobileNet v2+RF, MobileNet v2+KNN, MobileNet v2+AdaBoost, AlexNet, and MobileNet v2. For the recognition results of the three teas using reconstruction of wavelet components LL + HL + LH, the overall accuracy rate reached 98.7%, which was 4.7%, 3.4%, 1.4%, and 2.0% higher than that of LH + HL + HH, LL + HH + HH, LL + LL + HH, and LL + LL + LL. This research can provide new inspiration and technical support for grade and quality assessment of cross-category tea. Full article

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

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17 pages, 5089 KiB

Open AccessEditor’s ChoiceArticle

Calibration and Verification Test of Cinnamon Soil Simulation Parameters Based on Discrete Element Method

by Yiqing Qiu, Zhijun Guo, Xin Jin, Pangang Zhang, Shengjie Si and Fugui Guo

Agriculture 2022, 12(8), 1082; https://doi.org/10.3390/agriculture12081082 - 22 Jul 2022

Cited by 12 | Viewed by 1885

Abstract

To obtain the discrete element simulation model parameters suitable for the interaction between cinnamon soil and soil-engaging components, the Hertz–Mindlin with JKR contact model in EDEM simulation software was used to calibrate the relevant model parameters of cinnamon soil. Firstly, the particle size [...] Read more.

To obtain the discrete element simulation model parameters suitable for the interaction between cinnamon soil and soil-engaging components, the Hertz–Mindlin with JKR contact model in EDEM simulation software was used to calibrate the relevant model parameters of cinnamon soil. Firstly, the particle size distribution, moisture content, volume density, Poisson’ s ratio, shear modulus, and other parameters of the cinnamon soil were measured with cinnamon soil as the research object. Further, taking the stacking angle as the response value, the Plackett–Burman test, the steepest climbing test, and the Box–Behnken were designed by using the Design-Expert software to calibrate and optimize the physical parameters of soil simulation. The optimal parameter combination was obtained: cinnamon soil–cinnamon soil rolling friction coefficient was 0.08, soil JKR surface energy was 0.37 J/m⁻², and cinnamon soil–steel static friction coefficient was 0.64. Finally, the discrete element simulation verification test of stacking angle and cutting resistance was carried out under the calibrated parameters. The comparative calculation showed that the relative error between the simulated stacking angle and the measured stacking angle was 0.253%, and the maximum relative error between the simulated cutting resistance and the measured cutting resistance was 10.32%, which was within the acceptable range, indicating the high accuracy and reliability for the calibration parameters. The research results have important reference value for the energy-saving and consumption-reducing design of soil tillage components and provide basic data for the simulation of cutting resistance research of cinnamon-soil-engaging components. Full article

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

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17 pages, 1851 KiB

Open AccessEditor’s ChoiceArticle

Space Comparison of Agricultural Green Growth in Agricultural Modernization: Scale and Quality

by Tingting Huang and Bin Xiong

Agriculture 2022, 12(7), 1067; https://doi.org/10.3390/agriculture12071067 - 21 Jul 2022

Cited by 14 | Viewed by 3585

Abstract

Promoting agricultural green growth has become an indispensable key content to speed up the process of agricultural modernization, has become a necessary prerequisite to achieve common prosperity of the rural people, and has become the basic practice of implementing people-centered development thought in [...] Read more.

Promoting agricultural green growth has become an indispensable key content to speed up the process of agricultural modernization, has become a necessary prerequisite to achieve common prosperity of the rural people, and has become the basic practice of implementing people-centered development thought in the stage of high-quality development. Many researchers have studied the problems, level measurement and route choice of the growth of agriculture. However, there have been few studies on how to promote the agricultural green growth from the perspective of agricultural modernization, and how to combine the green agricultural GDP with the agricultural green total factor productivity (GTFP). To address this research inadequacy, in this paper, we focus on the time and space comparison of green agricultural GDP, agricultural GTFP, and their source decomposition, and summarize and discuss the key factors affecting agricultural GTFP. The results show that the share of output value of green agriculture in Tongren City is relatively high within the region of the province, and there is a large temporal and spatial difference between the change of agricultural GTFP and agricultural technology utilization efficiency and agricultural technology progress. At the same time, the improvement of economic development level can significantly promote the rise of agricultural GTFP, agricultural technology utilization efficiency, and agricultural technology progress. On balance, our results compare green agricultural GDP, agricultural GTFP, and their source decomposition in time and space, and reveals their evolution law and development trend from the perspective of high-quality development of agricultural modernization. In this way, we can provide an empirical basis and decision-making reference for accelerating the high-quality development of agricultural modernization. Full article

(This article belongs to the Special Issue Ecological Restoration and Rural Economic Development)

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17 pages, 4079 KiB

Open AccessEditor’s ChoiceArticle

Recommending Advanced Deep Learning Models for Efficient Insect Pest Detection

by Wei Li, Tengfei Zhu, Xiaoyu Li, Jianzhang Dong and Jun Liu

Agriculture 2022, 12(7), 1065; https://doi.org/10.3390/agriculture12071065 - 21 Jul 2022

Cited by 23 | Viewed by 4552

Abstract

Insect pest management is one of the main ways to improve the crop yield and quality in agriculture and it can accurately and timely detect insect pests, which is of great significance to agricultural production. In the past, most insect pest detection tasks [...] Read more.

Insect pest management is one of the main ways to improve the crop yield and quality in agriculture and it can accurately and timely detect insect pests, which is of great significance to agricultural production. In the past, most insect pest detection tasks relied on the experience of agricutural experts, which is time-consuming, laborious and subjective. In rencent years, various intelligent methods have emerged for detection. This paper employs three frontier Deep Convolutional Neural Network (DCNN) models—Faster-RCNN, Mask-RCNN and Yolov5, for efficient insect pest detection. In addition, we made two coco datasets by ourselves on the basis of Baidu AI insect detection dataset and IP102 dataset, and compared these three frontier deep learning models on the two coco datasets. In terms of Baidu AI insect detection dataset whose background is simple, the experimental results strongly recommend Yolov5 for the insect pest detection, because its accuracy reaches above 99% while Faster-RCNN’s and Mask-RCNN’s reach above 98%. Meanwhile, Yolov5 has the faster computational speed than Faster-RCNN and Mask-RCNN. Comparatively speaking, with regard to the IP102 dataset whose background is complex and categories are abundant, Faster-RCNN and Mask-RCNN have the higher accuracy, reaching 99%, than Yolov5 whose accuracy is about 97%. Full article

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

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14 pages, 4525 KiB

Open AccessEditor’s ChoiceArticle

Lightweight Detection Algorithm of Kiwifruit Based on Improved YOLOX-S

by Junchi Zhou, Wenwu Hu, Airu Zou, Shike Zhai, Tianyu Liu, Wenhan Yang and Ping Jiang

Agriculture 2022, 12(7), 993; https://doi.org/10.3390/agriculture12070993 - 09 Jul 2022

Cited by 18 | Viewed by 2795

Abstract

Considering the high requirements of current kiwifruit picking recognition systems for mobile devices, including the small number of available features for image targets and small-scale aggregation, an enhanced YOLOX-S target detection algorithm for kiwifruit picking robots is proposed in this study. This involved [...] Read more.

Considering the high requirements of current kiwifruit picking recognition systems for mobile devices, including the small number of available features for image targets and small-scale aggregation, an enhanced YOLOX-S target detection algorithm for kiwifruit picking robots is proposed in this study. This involved designing a new multi-scale feature integration structure in which, with the aim of providing a small and lightweight model, the feature maps used for detecting large targets in the YOLOX model are eliminated, the feature map of small targets is sampled through the nearest neighbor values, the superficial features are spliced with the final features, the gradient of the SiLU activation function is perturbed, and the loss function at the output is optimized. The experimental results show that, compared with the original YOLOX-S, the enhanced model improved the detection average precision (AP) of kiwifruit images by 6.52%, reduced the number of model parameters by 44.8%, and improved the model detection speed by 63.9%. Hence, with its outstanding effectiveness and relatively light weight, the proposed model is capable of effectively providing data support for the 3D positioning and automated picking of kiwifruit. It may also successfully provide solutions in similar fields related to small target detection. Full article

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

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17 pages, 2091 KiB

Open AccessEditor’s ChoiceArticle

Excessive Nitrogen Application Leads to Lower Rice Yield and Grain Quality by Inhibiting the Grain Filling of Inferior Grains

by Can Zhao, Guangming Liu, Yue Chen, Yan Jiang, Yi Shi, Lingtian Zhao, Pingqiang Liao, Weiling Wang, Ke Xu, Qigen Dai and Zhongyang Huo

Agriculture 2022, 12(7), 962; https://doi.org/10.3390/agriculture12070962 - 05 Jul 2022

Cited by 20 | Viewed by 3834

Abstract

Nitrogen fertilizer is an important agronomic measure to regulate rice yield and grain quality. Grain filling is crucial for the formation of rice yield and grain quality. However, there are few studies on the effects of excessive nitrogen application (ENA) on grain filling [...] Read more.

Nitrogen fertilizer is an important agronomic measure to regulate rice yield and grain quality. Grain filling is crucial for the formation of rice yield and grain quality. However, there are few studies on the effects of excessive nitrogen application (ENA) on grain filling rate and grain quality. A two-year field experiment was conducted to reveal the difference in grain filling characteristics and grain quality of superior grains (SG) and inferior grains (IG), as well as their responses to nitrogen fertilizer. We determined the grain appearance, the rice yield, the grain filling characteristics of SG and IG, and grain quality. We found that with the increasing nitrogen application level, grain yield of both varieties first increased and then decreased. The average yield of excessive nitrogen application (345 kg N ha⁻¹) was 2.68–6.31% lower than that of appropriate nitrogen application (270 kg N ha⁻¹). ENA reduced the grain filling rate by 12.7–25.8%, and the grain filling rate of SG was higher than that of IG. Increasing nitrogen application increased the processing quality and appearance quality of rice grain, but ENA deteriorated the appearance quality, eating quality and nutritional quality. The amylose content and taste value of SS were 3.1–9.7% and 7.1–20.2% higher than those of IS, respectively. The protein components of SG were lower than those of IG. Taken together, our results revealed that ENA leads to the lowering of rice grain yield and grain quality by suppressed grain filling of inferior grains. Full article

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

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19 pages, 4322 KiB

Open AccessEditor’s ChoiceArticle

Contributions of Plant Litter Decomposition to Soil Nutrients in Ecological Tea Gardens

by Shaqian Liu, Rui Yang, Xudong Peng, Chunlan Hou, Juebing Ma and Jiarui Guo

Agriculture 2022, 12(7), 957; https://doi.org/10.3390/agriculture12070957 - 03 Jul 2022

Cited by 17 | Viewed by 2676

Abstract

Plant litter decomposition and its effect on soil nutrients are important parts of the ecosystem material cycle, and understanding these processes is key for species selection and allocation to promote the effective use of litter in ecological tea gardens. In this study, the [...] Read more.

Plant litter decomposition and its effect on soil nutrients are important parts of the ecosystem material cycle, and understanding these processes is key for species selection and allocation to promote the effective use of litter in ecological tea gardens. In this study, the in situ litter decomposition method was used to examine the decomposition characteristics of leaf litter of Cinnamomum glanduliferum, Betula luminifera, Cunninghamia lanceolata, Pinus massoniana, and Camellia sinensis prunings in the Jiu’an ecological tea garden in Guizhou and their effects on soil nutrients. The results showed that the litter decomposition rate of broad-leaved tree species was higher than that of coniferous tree species, with a half-life of 1.11–1.75a and a turnover period of 4.79–7.57a. There are two release modes of nutrient release from litter: direct release and leaching–enrichment–release. Different litters make different contributions to soil nutrients; Betula luminifera and Cinnamomum glanduliferum litter increased the contents of soil organic carbon, soil total nitrogen, and soil hydrolyzed nitrogen. Betula luminifera litter increased the content of soil total phosphorus, soil available phosphorus, and soil available potassium, and Pinus massoniana litter increased the content of soil total potassium and soil available potassium; therefore, it is concluded that the decomposition of Betula luminifera litter had a positive effect on soil nutrient content. Thus, Betula luminifera is a good choice for inclusion in ecological tea gardens to increase their nutrient return capacity, maintain fertility, and generally promote the ecological development of tea gardens. Full article

(This article belongs to the Section Agricultural Soils)

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13 pages, 1458 KiB

Open AccessEditor’s ChoiceArticle

Sublethal Effects of Emamectin Benzoate on Fall Armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae)

by Zhuo-Kun Liu, Xue-Lin Li, Xiao-Feng Tan, Mao-Fa Yang, Atif Idrees, Jian-Feng Liu, Sai-Jie Song and Jian Shen

Agriculture 2022, 12(7), 959; https://doi.org/10.3390/agriculture12070959 - 03 Jul 2022

Cited by 17 | Viewed by 3690

Abstract

Fall armyworm (FAW), Spodoptera frugiperda (Lepidoptera: Noctuidae), is a highly invasive polyphagous pest that causes great economic losses to agricultural production. Emamectin benzoate (EMB) is one of the most popular biopesticides with high antipest, anti-parasitic and anti-nematode activities and low toxicity. The present [...] Read more.

Fall armyworm (FAW), Spodoptera frugiperda (Lepidoptera: Noctuidae), is a highly invasive polyphagous pest that causes great economic losses to agricultural production. Emamectin benzoate (EMB) is one of the most popular biopesticides with high antipest, anti-parasitic and anti-nematode activities and low toxicity. The present study was conducted to determine the lethality of EMB to FAW for 24 h. Sublethal effects of EMB on FAW parental and offspring generations were also assessed. LC₁₀, LC₂₀ and LC₅₀ EMB for 24 h on FAW third instar larvae were 0.0127 mg/L, 0.0589 mg/L, and 0.1062 mg/L, respectively. A low dose of sublethal concentrations of EMB could significantly influence the life cycle of FAW parental and offspring generations. Sublethal concentration (LC₂₀) of EMB significantly prolonged the pupal period of male and increased the pupal weight of male but not of female, and significantly delayed the oviposition period and longevity of adult FAW. In the FAW offspring generation, sublethal concentrations significantly increased the mortality of offspring pupae and pre-adults, and reduced the development time of offspring larvae and pre-adult male and female. Sublethal concentrations (LC₁₀ and LC₂₀) of EMB significantly decreased the FAW oviposition period. However, only LC₁₀ significantly reduced FAW F₁ female fecundity. No significant difference was found in the intrinsic rates of natural increase (r_m), finite rate of population increase (λ), and net reproductive rate (R₀) of FAW offspring exposed to sublethal concentrations. This is the first study to determine the sublethal concentrations of EMB on the life table parameters of two FAW generations. These findings can provide important implications for the rational utilization of FAW insecticides. Full article

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

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23 pages, 8066 KiB

Open AccessEditor’s ChoiceArticle

Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model

by Haiqing Wang, Shuqi Shang, Dongwei Wang, Xiaoning He, Kai Feng and Hao Zhu

Agriculture 2022, 12(7), 931; https://doi.org/10.3390/agriculture12070931 - 27 Jun 2022

Cited by 39 | Viewed by 7643

Abstract

Traditional plant disease diagnosis methods are mostly based on expert diagnosis, which easily leads to the backwardness of crop disease control and field management. In this paper, to improve the speed and accuracy of disease classification, a plant disease detection and classification method [...] Read more.

Traditional plant disease diagnosis methods are mostly based on expert diagnosis, which easily leads to the backwardness of crop disease control and field management. In this paper, to improve the speed and accuracy of disease classification, a plant disease detection and classification method based on the optimized lightweight YOLOv5 model is proposed. We propose an IASM mechanism to improve the accuracy and efficiency of the model, to achieve model weight reduction through Ghostnet and WBF structure, and to combine BiFPN and fast normalization fusion for weighted feature fusion to speed up the learning efficiency of each feature layer. To verify the effect of the optimized model, we conducted a performance comparison test and ablation test between the optimized model and other mainstream models. The results show that the operation time and accuracy of the optimized model are 11.8% and 3.98% higher than the original model, respectively, while F1 score reaches 92.65%, which highlight statistical metrics better than the current mainstream models. Moreover, the classification accuracy rate on the self-made dataset reaches 92.57%, indicating the effectiveness of the plant disease classification model proposed in this paper, and the transfer learning ability of the model can be used to expand the application scope in the future. Full article

(This article belongs to the Section Digital Agriculture)

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18 pages, 2032 KiB

Open AccessEditor’s ChoiceArticle

Modeling the Water and Nitrogen Management Practices in Paddy Fields with HYDRUS-1D

by Kaiwen Chen, Shuang’en Yu, Tao Ma, Jihui Ding, Pingru He, Yao Li, Yan Dai and Guangquan Zeng

Agriculture 2022, 12(7), 924; https://doi.org/10.3390/agriculture12070924 - 26 Jun 2022

Cited by 12 | Viewed by 2535

Abstract

Rice production involves abundant water and fertilizer inputs and is prone to nitrogen (N) loss via surface runoff and leaching, resulting in agricultural diffuse pollution. Based on a two-season paddy field experiment in Jiangsu Province, China, field water and N dynamics and their [...] Read more.

Rice production involves abundant water and fertilizer inputs and is prone to nitrogen (N) loss via surface runoff and leaching, resulting in agricultural diffuse pollution. Based on a two-season paddy field experiment in Jiangsu Province, China, field water and N dynamics and their balances were determined with the well-calibrated HYDRUS-1D model. Then, scenarios of different controlled drainage and N fertilizer applications were simulated using the HYDRUS-1D model to analyze the features and factors of N loss from paddy fields. Evapotranspiration and deep percolation were the two dominant losses of total water input over the two seasons, with an average loss of 50.9% and 38.8%, respectively. Additionally, gaseous loss of N from the whole soil column accounted for more than half of total N input on average, i.e., ammonia volatilization (17.5% on average for two seasons) and denitrification (39.7%), while the N uptake by rice accounted for 37.1% on average. The ratio of N loss via surface runoff to total N input exceeded 20% when the N fertilizer rate reached 300 kg ha⁻¹. More and longer rainwater storage in rice fields under controlled drainage reduced surface runoff losses but increased the risk of groundwater contamination by N leaching. Therefore, compared with raising the maximum ponding rainwater depth for controlled drainage, optimizing N fertilizer inputs may be more beneficial for controlling agricultural diffuse pollution by reducing N loss via surface runoff and leaching. The HYDRUS-1D model provides an approach for the quantitative decision-making process of sustainable agricultural water and N management. Full article

(This article belongs to the Special Issue Water-Saving Irrigation Technology and Strategies for Crop Production)

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13 pages, 3070 KiB

Open AccessEditor’s ChoiceArticle

Improving Wheat Yield Prediction Accuracy Using LSTM-RF Framework Based on UAV Thermal Infrared and Multispectral Imagery

by Yulin Shen, Benoît Mercatoris, Zhen Cao, Paul Kwan, Leifeng Guo, Hongxun Yao and Qian Cheng

Agriculture 2022, 12(6), 892; https://doi.org/10.3390/agriculture12060892 - 20 Jun 2022

Cited by 25 | Viewed by 3669

Abstract

Yield prediction is of great significance in agricultural production. Remote sensing technology based on unmanned aerial vehicles (UAVs) offers the capacity of non-intrusive crop yield prediction with low cost and high throughput. In this study, a winter wheat field experiment with three levels [...] Read more.

Yield prediction is of great significance in agricultural production. Remote sensing technology based on unmanned aerial vehicles (UAVs) offers the capacity of non-intrusive crop yield prediction with low cost and high throughput. In this study, a winter wheat field experiment with three levels of irrigation (T1 = 240 mm, T2 = 190 mm, T3 = 145 mm) was conducted in Henan province. Multispectral vegetation indices (VIs) and canopy water stress indices (CWSI) were obtained using an UAV equipped with multispectral and thermal infrared cameras. A framework combining a long short-term memory neural network and random forest (LSTM-RF) was proposed for predicting wheat yield using VIs and CWSI from multi-growth stages as predictors. Validation results showed that the R² of 0.61 and the RMSE value of 878.98 kg/ha was achieved in predicting grain yield using LSTM. LSTM-RF model obtained better prediction results compared to the LSTM with n R² of 0.78 and RMSE of 684.1 kg/ha, which is equivalent to a 22% reduction in RMSE. The results showed that LSTM-RF considered both the time-series characteristics of the winter wheat growth process and the non-linear characteristics between remote sensing data and crop yield data, providing an alternative for accurate yield prediction in modern agricultural management. Full article

(This article belongs to the Special Issue Data-Driven Agricultural Innovations with Artificial Intelligence and Industrial Internet of Things (IIoT))

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17 pages, 7466 KiB

Open AccessEditor’s ChoiceArticle

GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

by Jianwu Lin, Xiaoyulong Chen, Renyong Pan, Tengbao Cao, Jitong Cai, Yang Chen, Xishun Peng, Tomislav Cernava and Xin Zhang

Agriculture 2022, 12(6), 887; https://doi.org/10.3390/agriculture12060887 - 20 Jun 2022

Cited by 33 | Viewed by 4898

Abstract

Most convolutional neural network (CNN) models have various difficulties in identifying crop diseases owing to morphological and physiological changes in crop tissues, and cells. Furthermore, a single crop disease can show different symptoms. Usually, the differences in symptoms between early crop disease and [...] Read more.

Most convolutional neural network (CNN) models have various difficulties in identifying crop diseases owing to morphological and physiological changes in crop tissues, and cells. Furthermore, a single crop disease can show different symptoms. Usually, the differences in symptoms between early crop disease and late crop disease stages include the area of disease and color of disease. This also poses additional difficulties for CNN models. Here, we propose a lightweight CNN model called GrapeNet for the identification of different symptom stages for specific grape diseases. The main components of GrapeNet are residual blocks, residual feature fusion blocks (RFFBs), and convolution block attention modules. The residual blocks are used to deepen the network depth and extract rich features. To alleviate the CNN performance degradation associated with a large number of hidden layers, we designed an RFFB module based on the residual block. It fuses the average pooled feature map before the residual block input and the high-dimensional feature maps after the residual block output by a concatenation operation, thereby achieving feature fusion at different depths. In addition, the convolutional block attention module (CBAM) is introduced after each RFFB module to extract valid disease information. The obtained results show that the identification accuracy was determined as 82.99%, 84.01%, 82.74%, 84.77%, 80.96%, 82.74%, 80.96%, 83.76%, and 86.29% for GoogLeNet, Vgg16, ResNet34, DenseNet121, MobileNetV2, MobileNetV3_large, ShuffleNetV2_×1.0, EfficientNetV2_s, and GrapeNet. The GrapeNet model achieved the best classification performance when compared with other classical models. The total number of parameters of the GrapeNet model only included 2.15 million. Compared with DenseNet121, which has the highest accuracy among classical network models, the number of parameters of GrapeNet was reduced by 4.81 million, thereby reducing the training time of GrapeNet by about two times compared with that of DenseNet121. Moreover, the visualization results of Grad-cam indicate that the introduction of CBAM can emphasize disease information and suppress irrelevant information. The overall results suggest that the GrapeNet model is useful for the automatic identification of grape leaf diseases. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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12 pages, 23565 KiB

Open AccessEditor’s ChoiceArticle

Finite Element Model Construction and Cutting Parameter Calibration of Wild Chrysanthemum Stem

by Tao Wang, Zhengdao Liu, Xiaoli Yan, Guopeng Mi, Suyuan Liu, Kezhou Chen, Shilin Zhang, Xun Wang, Shuo Zhang and Xiaopeng Wu

Agriculture 2022, 12(6), 894; https://doi.org/10.3390/agriculture12060894 - 20 Jun 2022

Cited by 13 | Viewed by 2136

Abstract

Due to a lack of an accurate model in finite element simulation of mechanized harvesting of wild chrysanthemum, the stem of wild chrysanthemum in the harvesting period is taken as the research object. ANSYS Workbench 19.0 software and LS-DYNA software (LS-PrePOST-4.3-X64) are used [...] Read more.

Due to a lack of an accurate model in finite element simulation of mechanized harvesting of wild chrysanthemum, the stem of wild chrysanthemum in the harvesting period is taken as the research object. ANSYS Workbench 19.0 software and LS-DYNA software (LS-PrePOST-4.3-X64) are used to calibrate the finite element simulation model of wild chrysanthemum stem cutting. The stem diameter distribution at the cutting height of the chrysanthemum is obtained. The maximum shear forces at different diameters (7 mm, 8 mm, 9 mm, 10 mm, and 11 mm) within the cutting range are determined as 120.0 N, 159.2 N, 213.8 N, 300.0 N, and 378.2 N, respectively, by using a biomechanical testing machine and a custom-made shear blade. The Plastic_Kinematic failure model is used to simulate the cutting process by the finite element method. The Plackett–Burman test is employed to screen out the test factors that significantly affect the results, namely, the yield stress, failure strain, and strain rate parameter C. The regression model between the shear force and significant parameters is obtained by central composite design experiments. To obtain the model parameters, the measured values are substituted into the regression equation as the simulation target values. In other words, the yield stress is 17.96 MPa, the strain rate parameter C is 87.27, and the failure strain is 0.0387. The maximum shear force simulation test is carried out with the determined parameters. The results showed that the maximum error between the simulated and the actual value of the maximum shear force of wild chrysanthemum stems with different diameters is 7.8%. This indicates that the calibrated parameters of the relevant stem failure model can be used in the finite element method simulation and provide a basis for subsequent simulations. Full article

(This article belongs to the Special Issue Advances in Agricultural Engineering Technologies and Application)

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24 pages, 335 KiB

Open AccessEditor’s ChoiceReview

Post-Anthesis Heat Influences Grain Yield, Physical and Nutritional Quality in Wheat: A Review

by Edward Fernie, Daniel K. Y. Tan, Sonia Y. Liu, Najeeb Ullah and Ali Khoddami

Agriculture 2022, 12(6), 886; https://doi.org/10.3390/agriculture12060886 - 19 Jun 2022

Cited by 16 | Viewed by 3383

Abstract

Climate change threatens to impact wheat productivity, quality and global food security. Maintaining crop productivity under abiotic stresses such as high temperature is therefore imperative to managing the nutritional needs of a growing global population. The article covers the current knowledge on the [...] Read more.

Climate change threatens to impact wheat productivity, quality and global food security. Maintaining crop productivity under abiotic stresses such as high temperature is therefore imperative to managing the nutritional needs of a growing global population. The article covers the current knowledge on the impact of post-anthesis heat on grain yield and quality of wheat crops. The objectives of the current article were to review (1) the effect of post-anthesis heat stress events (above 30.0 °C) on wheat grain yield, (2) the effect of heat stress on both the physical and chemical quality of wheat grain during grain development, (3) identify wheat cultivars that display resilience to heat stress and (4) address gaps within the literature and provide a direction for future research. Heat stress events at the post-anthesis stage impacted wheat grain yield mostly at the grain filling stage, whilst the effect on physical and chemical quality was varied. The overall effect of post-anthesis heat on wheat yield and quality was genotype-specific. Additionally, heat tolerance mechanisms were identified that may explain variations in yield and quality data obtained between studies. Full article

(This article belongs to the Topic The Effect of Climate Change on Crops and Natural Ecosystems)

13 pages, 5609 KiB

Open AccessEditor’s ChoiceArticle

Soil Electrical Conductivity and Satellite-Derived Vegetation Indices for Evaluation of Phosphorus, Potassium and Magnesium Content, pH, and Delineation of Within-Field Management Zones

by Piotr Mazur, Dariusz Gozdowski and Elżbieta Wójcik-Gront

Agriculture 2022, 12(6), 883; https://doi.org/10.3390/agriculture12060883 - 19 Jun 2022

Cited by 17 | Viewed by 3243

Abstract

The optimization of soil sampling is very important in precision agriculture. The main aim of this study was to evaluate the relationships between selected spectral indices (NDWI—normalized difference water index and NDVI—normalized difference vegetation index) and apparent soil electrical conductivity (EC) with soil [...] Read more.

The optimization of soil sampling is very important in precision agriculture. The main aim of this study was to evaluate the relationships between selected spectral indices (NDWI—normalized difference water index and NDVI—normalized difference vegetation index) and apparent soil electrical conductivity (EC) with soil nutrient content (phosphorus, potassium, and magnesium) and pH. Moreover, the usefulness of these variables for the delineation of within-field management zones was assessed. The study was conducted in 2021 in central Poland at three maize fields with a total area approximately 100 ha. The analyses were performed based on 47 management zones, which were used for soil sampling. Significant positive correlations were observed between the NDVI for the bare soil and all the studied nutrient contents in the soil and pH. A very strong positive correlation was observed between the soil EC and the potassium content and a moderate correlation was found with the magnesium content. A multiple-regression analysis proved that the soil nutrient content, especially potassium and phosphorus, was strongly related to the EC and NDVI. The novelty of this study is that it proves the relationships between soil and the crop attributes, EC and NDVI, which can be measured at field scale relatively simply, and the crucial soil nutrients, phosphorus and potassium. This allows the results to be used for optimized variable-rate fertilization. Full article

(This article belongs to the Special Issue Precision Agriculture Adoption Strategies)

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16 pages, 5104 KiB

Open AccessEditor’s ChoiceArticle

Transformer Help CNN See Better: A Lightweight Hybrid Apple Disease Identification Model Based on Transformers

by Xiaopeng Li and Shuqin Li

Agriculture 2022, 12(6), 884; https://doi.org/10.3390/agriculture12060884 - 19 Jun 2022

Cited by 30 | Viewed by 4123

Abstract

The complex backgrounds of crop disease images and the small contrast between the disease area and the background can easily cause confusion, which seriously affects the robustness and accuracy of apple disease- identification models. To solve the above problems, this paper proposes a [...] Read more.

The complex backgrounds of crop disease images and the small contrast between the disease area and the background can easily cause confusion, which seriously affects the robustness and accuracy of apple disease- identification models. To solve the above problems, this paper proposes a Vision Transformer-based lightweight apple leaf disease- identification model, ConvViT, to extract effective features of crop disease spots to identify crop diseases. Our ConvViT includes convolutional structures and Transformer structures; the convolutional structure is used to extract the global features of the image, and the Transformer structure is used to obtain the local features of the disease region to help the CNN see better. The patch embedding method is improved to retain more edge information of the image and promote the information exchange between patches in the Transformer. The parameters and FLOPs (Floating Point Operations) of the model are significantly reduced by using depthwise separable convolution and linear-complexity multi-head attention operations. Experimental results on a complex background of a self-built apple leaf disease dataset show that ConvViT achieves comparable identification results (96.85%) with the current performance of the state-of-the-art Swin-Tiny. The parameters and FLOPs are only 32.7% and 21.7% of Swin-Tiny, and significantly ahead of MobilenetV3, Efficientnet-b0, and other models, which indicates that the proposed model is indeed an effective disease-identification model with practical application value. Full article

(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)

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18 pages, 11631 KiB

Open AccessEditor’s ChoiceArticle

A Real-Time Apple Targets Detection Method for Picking Robot Based on ShufflenetV2-YOLOX

by Wei Ji, Yu Pan, Bo Xu and Juncheng Wang

Agriculture 2022, 12(6), 856; https://doi.org/10.3390/agriculture12060856 - 13 Jun 2022

Cited by 38 | Viewed by 4651

Abstract

In order to enable the picking robot to detect and locate apples quickly and accurately in the orchard natural environment, we propose an apple object detection method based on Shufflenetv2-YOLOX. This method takes YOLOX-Tiny as the baseline and uses the lightweight network Shufflenetv2 [...] Read more.

In order to enable the picking robot to detect and locate apples quickly and accurately in the orchard natural environment, we propose an apple object detection method based on Shufflenetv2-YOLOX. This method takes YOLOX-Tiny as the baseline and uses the lightweight network Shufflenetv2 added with the convolutional block attention module (CBAM) as the backbone. An adaptive spatial feature fusion (ASFF) module is added to the PANet network to improve the detection accuracy, and only two extraction layers are used to simplify the network structure. The average precision (AP), precision, recall, and F1 of the trained network under the verification set are 96.76%, 95.62%, 93.75%, and 0.95, respectively, and the detection speed reaches 65 frames per second (FPS). The test results show that the AP value of Shufflenetv2-YOLOX is increased by 6.24% compared with YOLOX-Tiny, and the detection speed is increased by 18%. At the same time, it has a better detection effect and speed than the advanced lightweight networks YOLOv5-s, Efficientdet-d0, YOLOv4-Tiny, and Mobilenet-YOLOv4-Lite. Meanwhile, the half-precision floating-point (FP16) accuracy model on the embedded device Jetson Nano with TensorRT acceleration can reach 26.3 FPS. This method can provide an effective solution for the vision system of the apple picking robot. Full article

(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)

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12 pages, 576 KiB

Open AccessEditor’s ChoiceReview

Rapeseed Meal and Its Application in Pig Diet: A Review

by Hao Cheng, Xiang Liu, Qingrui Xiao, Fan Zhang, Nian Liu, Lizi Tang, Jing Wang, Xiaokang Ma, Bie Tan, Jiashun Chen and Xianren Jiang

Agriculture 2022, 12(6), 849; https://doi.org/10.3390/agriculture12060849 - 12 Jun 2022

Cited by 15 | Viewed by 3468

Abstract

Rapeseed is the second largest plant protein resource in the world with an ideal profile of essential amino acids. Rapeseed meal (RSM) is one of the by-products of rapeseed oil extraction. Due to the anti-nutritional components (glucosinolates and fiber) and poor palatability, RSM [...] Read more.

Rapeseed is the second largest plant protein resource in the world with an ideal profile of essential amino acids. Rapeseed meal (RSM) is one of the by-products of rapeseed oil extraction. Due to the anti-nutritional components (glucosinolates and fiber) and poor palatability, RSM is limited in livestock diets. Recently, how to decrease the anti-nutritional factors and improve the nutritional value of RSM has become a hot topic. Therefore, the major components of RSM have been reviewed with emphasis on the methods to improve the nutritional value of RSM as well as the application of RSM in pig diets. Full article

(This article belongs to the Special Issue Improvement of Nutritional Utilization of Alternative Feed Ingredients in Pigs)

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18 pages, 1106 KiB

Open AccessEditor’s ChoiceReview

A Platform Approach to Smart Farm Information Processing

by Mohammad Amiri-Zarandi, Mehdi Hazrati Fard, Samira Yousefinaghani, Mitra Kaviani and Rozita Dara

Agriculture 2022, 12(6), 838; https://doi.org/10.3390/agriculture12060838 - 10 Jun 2022

Cited by 37 | Viewed by 10656

Abstract

With the rapid growth of population and the increasing demand for food worldwide, improving productivity in farming procedures is essential. Smart farming is a concept that emphasizes the use of modern technologies such as the Internet of Things (IoT) and artificial intelligence (AI) [...] Read more.

With the rapid growth of population and the increasing demand for food worldwide, improving productivity in farming procedures is essential. Smart farming is a concept that emphasizes the use of modern technologies such as the Internet of Things (IoT) and artificial intelligence (AI) to enhance productivity in farming practices. In a smart farming scenario, large amounts of data are collected from diverse sources such as wireless sensor networks, network-connected weather stations, monitoring cameras, and smartphones. These data are valuable resources to be used in data-driven services and decision support systems (DSS) in farming applications. However, one of the major challenges with these large amounts of agriculture data is their immense diversity in terms of format and meaning. Moreover, the different services and technologies in a smart farming ecosystem have limited capability to work together due to the lack of standardized practices for data and system integration. These issues create a significant challenge in cooperative service provision, data and technology integration, and data-sharing practices. To address these issues, in this paper, we propose the platform approach, a design approach intended to guide building effective, reliable, and robust smart farming systems. The proposed platform approach considers six requirements for seamless integration, processing, and use of farm data. These requirements in a smart farming platform include interoperability, reliability, scalability, real-time data processing, end-to-end security and privacy, and standardized regulations and policies. A smart farming platform that considers these requirements leads to increased productivity, profitability, and performance of connected smart farms. In this paper, we aim at introducing the platform approach concept for smart farming and reviewing the requirements for this approach. Full article

(This article belongs to the Topic Grand Challenges of Advanced Technologies in Sustainable Agriculture 4.0: Future Farming, Harvesting and Preservation)

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11 pages, 276 KiB

Open AccessEditor’s ChoiceOpinion

Food Production in the Context of Global Developmental Challenges

by Tomasz Daszkiewicz

Agriculture 2022, 12(6), 832; https://doi.org/10.3390/agriculture12060832 - 09 Jun 2022

Cited by 24 | Viewed by 4714

Abstract

The article presents a synthetic analysis of the most pressing challenges associated with food security in the context of changes induced by global development and the generated problems. The study demonstrated that a more effective model of food production and management is needed [...] Read more.

The article presents a synthetic analysis of the most pressing challenges associated with food security in the context of changes induced by global development and the generated problems. The study demonstrated that a more effective model of food production and management is needed to counteract anthropogenic pressure on the natural environment and excessive exploitation of limited resources caused by rapid population growth. Policies aiming to increase the efficiency of production and conversion of raw materials into finished food products of plant and animal origin (including feed conversion into high-energy and high-protein foods), promote the use of novel protein sources for feed and food production, and prevent excessive food consumption and waste are needed. At present and in the future, demographic, social, environmental, and geopolitical factors as well as the availability of natural resources should be taken into account by world leaders who should act together, with solidarity, to provide food to countries suffering from food shortage. Adequate food availability, including both physical and financial access to food, cannot be guaranteed without a holistic approach to global food security. Full article

(This article belongs to the Section Crop Production)

20 pages, 929 KiB

Open AccessEditor’s ChoiceArticle

Impacts of Risk Perception and Environmental Regulation on Farmers’ Sustainable Behaviors of Agricultural Green Production in China

by Mingyue Li, Yu Liu, Yuhe Huang, Lianbei Wu and Kai Chen

Agriculture 2022, 12(6), 831; https://doi.org/10.3390/agriculture12060831 - 09 Jun 2022

Cited by 17 | Viewed by 2355

Abstract

In China, the excessive application and improper disposal of chemical inputs have posed a great threat to the agricultural ecological environment and human health. The key to solve this problem is to promote the sustainable behaviors of farmers’ agricultural green production (AGP). Based [...] Read more.

In China, the excessive application and improper disposal of chemical inputs have posed a great threat to the agricultural ecological environment and human health. The key to solve this problem is to promote the sustainable behaviors of farmers’ agricultural green production (AGP). Based on the micro-survey data of 652 farmers, this study adopts the binary probit model to investigate the impacts of risk perception and environmental regulation on the sustainable behaviors of farmers’ AGP. Results show that both risk perception and environmental regulation have significant effects on farmers’ willingness to engage in sustainable behaviors. Moreover, environmental regulation can positively adjust risk perception to improve farmers’ willingness to engage in sustainable behaviors. In terms of the two-dimensional variables, economic risks create the greatest negative impacts, and their marginal effect is 7.3%, while voluntary regulation creates the strongest positive impacts, and its marginal effect is 14.1%. However, both constrained and voluntary regulation have an enhanced moderating effect, where the effects of voluntary regulation are more remarkable. This is mainly because the environmental regulation policy signed by the government and farmers through the letter of commitment can inspire farmers to continue to implement green agricultural production from the deep heart. Therefore, government policies should constantly reduce farmers’ risk perception in terms of economic input, and adopt restrictive behaviors measures, such as regulatory punishment and voluntary contract, to promote their sustainable behaviors of AGP to the maximum extent. Full article

(This article belongs to the Special Issue Ecological Restoration and Rural Economic Development)

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11 pages, 286 KiB

Open AccessEditor’s ChoiceArticle

Milk Thistle (Silybum marianum), Marine Algae (Spirulina platensis) and Toxin Binder Powders in the Diets of Broiler Chickens Exposed to Aflatoxin-B1: Growth Performance, Humoral Immune Response and Cecal Microbiota

by Mostafa Feshanghchi, Payam Baghban-Kanani, Bahman Kashefi-Motlagh, Fariba Adib, Saba Azimi-Youvalari, Babak Hosseintabar-Ghasemabad, Marina Slozhenkina, Ivan Gorlov, Márcio G. Zangeronimo, Ayman A. Swelum, Alireza Seidavi, Rifat U. Khan, Marco Ragni, Vito Laudadio and Vincenzo Tufarelli

Agriculture 2022, 12(6), 805; https://doi.org/10.3390/agriculture12060805 - 02 Jun 2022

Cited by 20 | Viewed by 2761

Abstract

This research was performed to investigate the effects of milk thistle (MT), toxin binder (TB) and marine algae (Spirulina platensis; SP) on the performance, blood indices, humoral immunity and cecal microbiota of broiler chickens exposed to aflatoxin-B₁ (AFB1). A total [...] Read more.

This research was performed to investigate the effects of milk thistle (MT), toxin binder (TB) and marine algae (Spirulina platensis; SP) on the performance, blood indices, humoral immunity and cecal microbiota of broiler chickens exposed to aflatoxin-B₁ (AFB1). A total of 300 one-day-old male chicks were equally divided into five treatments, with six replicates with 10 birds per treatment. Dietary treatments included: (T₁) a control diet (without any feed additive or AFB1); (T₂) control diet + 0.6 mg AFB₁/kg; (T₃) T₂ + 10 g/kg MT; (T₄) T₂ + 1 g/kg TB; and (T₅) T₂ + 10 g/kg SP. BWG and FI were found to be considerably reduced in broilers given AFB1-contaminated diets (p < 0.05). The FCR was negatively influenced in birds fed AFB1-contaminated diets (p < 0.05). MT, TB, and SP powders also reduced the deleterious effects of AFB1 on the growth of chickens (p < 0.05). In comparison with the control birds and the other treatments, broilers given AFB1-contaminated diets had a higher relative weight of abdominal fat (p < 0.05). The feeding of AFB1 resulted in a substantial rise in AST and ALT activity (p < 0.05). MT, TB, and SP powders significantly decreased blood AST and ALT activity in broilers (p < 0.05). The AFB1 and MT groups had the lowest skin thickness (p < 0.05) twenty-four hours after injection. The phytohemagglutinin injection results showed that the TB and SP were more efficient than the other additives in removing toxins from the feed sources (p < 0.05). The antibody titer against sheep red blood cells (SRBCs) was lower in the AFB1 group compared to the control group at 28 days of age (p < 0.05). When comparing AFB1-fed chicks to the control treatment, there was a significant (p < 0.05) concentration of cecal Coliform bacteria. When MT, TB, and SP powders were added to AFB1-contaminated diet, cecal Coliforms were decreased (p < 0.05). When fed AFB1-contaminated diets, it can be concluded that MT, TB, and SP are suitable for supporting growth performance, immunological function, and the serum biochemical parameters of broiler chickens. Full article

(This article belongs to the Special Issue Effects of Alternative Feeds and Dietary Additives on Poultry Growth, Immune Function and Gut Health)

28 pages, 1915 KiB

Open AccessEditor’s ChoiceArticle

Research on the Time-Dependent Split Delivery Green Vehicle Routing Problem for Fresh Agricultural Products with Multiple Time Windows

by Daqing Wu and Chenxiang Wu

Agriculture 2022, 12(6), 793; https://doi.org/10.3390/agriculture12060793 - 30 May 2022

Cited by 70 | Viewed by 3942

Abstract

Due to the diversity and the different distribution conditions of agricultural products, split delivery plays an important role in the last mile distribution of agricultural products distribution. The time-dependent split delivery green vehicle routing problem with multiple time windows (TDSDGVRPMTW) is studied by [...] Read more.

Due to the diversity and the different distribution conditions of agricultural products, split delivery plays an important role in the last mile distribution of agricultural products distribution. The time-dependent split delivery green vehicle routing problem with multiple time windows (TDSDGVRPMTW) is studied by considering both economic cost and customer satisfaction. A calculation method for road travel time across time periods was designed. A satisfaction measure function based on a time window and a measure function of the economic cost was employed by considering time-varying vehicle speeds, fuel consumption, carbon emissions and customers’ time windows. The object of the TDSDGVRPMTW model is to minimize the sum of the economic cost and maximize average customer satisfaction. According to the characteristics of the model, a variable neighborhood search combined with a non-dominated sorting genetic algorithm II (VNS-NSGA-II) was designed. Finally, the experimental data show that the proposed approaches effectively reduce total distribution costs and promote energy conservation and customer satisfaction. Full article

(This article belongs to the Special Issue Internet and Computers for Agriculture)

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28 pages, 3036 KiB

Open AccessEditor’s ChoiceReview

Rice for Food Security: Revisiting Its Production, Diversity, Rice Milling Process and Nutrient Content

by Nur Atikah Mohidem, Norhashila Hashim, Rosnah Shamsudin and Hasfalina Che Man

Agriculture 2022, 12(6), 741; https://doi.org/10.3390/agriculture12060741 - 24 May 2022

Cited by 61 | Viewed by 20095

Abstract

Rice is food consumed regularly and is vital for the food security of over half the world’s population. Rice production on a global scale is predicted to rise by 58 to 567 million tonnes (Mt) by 2030. Rice contains a significant number of [...] Read more.

Rice is food consumed regularly and is vital for the food security of over half the world’s population. Rice production on a global scale is predicted to rise by 58 to 567 million tonnes (Mt) by 2030. Rice contains a significant number of calories and a wide variety of essential vitamins, minerals, and other nutritional values. Its nutrients are superior to those found in maize, wheat, and potatoes. It is also recognised as a great source of vitamin E and B5 as well as carbohydrates, thiamine, calcium, folate, and iron. Phytic acid and phenols are among the phenolic compounds found in rice, alongside sterols, flavonoids, terpenoids, anthocyanins, tocopherols, tocotrienols, and oryzanol. These compounds have been positively linked to antioxidant properties and have been shown to help prevent cardiovascular disease and diabetes. This review examines recent global rice production, selected varieties, consumption, ending stocks, and the composition of rice grains and their nutritional values. This review also includes a new method of paddy storage, drying, and grading of rice. Finally, the environmental impacts concerning rice cultivation are discussed, along with the obstacles that must be overcome and the current policy directions of rice-producing countries. Full article

(This article belongs to the Topic Sustainable Development and Food Insecurity)

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65 pages, 8395 KiB

Open AccessEditor’s ChoiceReview

A Comprehensive Review of Organochlorine Pesticide Monitoring in Agricultural Soils: The Silent Threat of a Conventional Agricultural Past

by Evangelia N. Tzanetou and Helen Karasali

Agriculture 2022, 12(5), 728; https://doi.org/10.3390/agriculture12050728 - 21 May 2022

Cited by 21 | Viewed by 6900

Abstract

Soil constitutes the central environmental compartment that, primarily due to anthropogenic activities, is the recipient of several contaminants. Among these are organochlorine pesticides (OCPs), which are of major concern, even though they were banned decades ago due to their persistence and the health [...] Read more.

Soil constitutes the central environmental compartment that, primarily due to anthropogenic activities, is the recipient of several contaminants. Among these are organochlorine pesticides (OCPs), which are of major concern, even though they were banned decades ago due to their persistence and the health effects they can elicit. In this review, an overview of monitoring studies regarding OCPs in soils published over the last 30 years along with the development of analytical methods and extraction procedures for their determination in soil are presented. The presented synopsis verifies the soil contamination by OCPs during the last several decades. Soil pollution by OCPs should be an essential aspect of the characterization of whole soil quality, considering that a significant percent of soils on a global scale are in the borderline of suitability for cultivation and pertinent activities. The latter, to an extent, is attributed to the presence of organic contaminants, especially those of persistent chemical natures. Full article

(This article belongs to the Special Issue Emerging Soil Pollutants: Detection, Risk Assessment, and Remediation)

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18 pages, 574 KiB

Open AccessEditor’s ChoiceReview

Emerging Precision Management Methods in Poultry Sector

by Katarzyna Olejnik, Ewa Popiela and Sebastian Opaliński

Agriculture 2022, 12(5), 718; https://doi.org/10.3390/agriculture12050718 - 18 May 2022

Cited by 13 | Viewed by 6838

Abstract

New approach to improve welfare in the poultry sector is targeted at the precise management of animals. In poultry production, we observe that birds’ health and quality of poultry products depend significantly on good welfare conditions, affecting economic efficiency. Using technology solutions in [...] Read more.

New approach to improve welfare in the poultry sector is targeted at the precise management of animals. In poultry production, we observe that birds’ health and quality of poultry products depend significantly on good welfare conditions, affecting economic efficiency. Using technology solutions in different systems of animal production is an innovation that can help farmers more effectively control the environmental conditions and health of birds. In addition, rising public concern about poultry breeding and welfare leads to developing solutions to increase the efficiency of control and monitoring in this animal production branch. Precision livestock farming (PLF) collects real-time data of birds using different types of technologies for this process. It means that PLF can help prevent lowering animal welfare by detecting early stages of diseases and stressful situations during birds’ management and allows steps to be taken quickly enough to limit the adverse effects. This review shows connections between the possibilities of using the latest technologies to monitor laying hens and broilers in developing precision livestock farming. Full article

(This article belongs to the Special Issue Animal Hygiene on Farms - Realising Animal Health Prevention)

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17 pages, 3528 KiB

Open AccessEditor’s ChoiceReview

Review of Material Parameter Calibration Method

by Weiquan Fang, Xinzhong Wang, Dianlei Han and Xuegeng Chen

Agriculture 2022, 12(5), 706; https://doi.org/10.3390/agriculture12050706 - 17 May 2022

Cited by 16 | Viewed by 2960

Abstract

The discrete element method and simulation analysis of the interaction between granular materials and implements provide a convenient and effective method for the optimal design of farming machinery. However, the parameter differences between different materials make discrete element simulation impossible to carry out [...] Read more.

The discrete element method and simulation analysis of the interaction between granular materials and implements provide a convenient and effective method for the optimal design of farming machinery. However, the parameter differences between different materials make discrete element simulation impossible to carry out directly. It is necessary to obtain the specific material parameters and contact parameters through parameter calibration of the simulation object, so as to make the simulation results more reliable. Parameter calibration mainly includes intrinsic parameter measurement, contact model selection, contact parameter selection, and parameter calibration. The test methods of the calibration test include the Plackett–Burman test and other methods of screening parameters with significant influence, and then selecting the optimal parameters through the climbing test, response surface analysis method, etc., and finally carrying out the regression analysis. This paper will describe the existing parameter measurement methods and parameter calibration methods and provide a reference for the scholars who study parameter calibration to carry out parameter calibration. Full article

(This article belongs to the Topic Emerging Agricultural Engineering Sciences, Technologies, and Applications)

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24 pages, 2764 KiB

Open AccessEditor’s ChoiceArticle

Evaluation of Agricultural Water Resources Carrying Capacity and Its Influencing Factors: A Case Study of Townships in the Arid Region of Northwest China

by Penglong Wang, Yao Wei, Fanglei Zhong, Xiaoyu Song, Bao Wang and Qinhua Wang

Agriculture 2022, 12(5), 700; https://doi.org/10.3390/agriculture12050700 - 16 May 2022

Cited by 15 | Viewed by 2225

Abstract

The water resources carrying capacity (WRCC) strongly determines the agricultural development in arid areas. Evaluation of WRCC is important in balancing the availability of water resources with society’s economic and environmental demands. Given the demand for sustainable utilization of agricultural water resources, we [...] Read more.

The water resources carrying capacity (WRCC) strongly determines the agricultural development in arid areas. Evaluation of WRCC is important in balancing the availability of water resources with society’s economic and environmental demands. Given the demand for sustainable utilization of agricultural water resources, we combine the water stress index and comprehensive index of WRCC and use multi-source data to evaluate agricultural WRCC and its influencing factors at the township scale. It makes up for the deficiencies of current research, such as the existence of single-index evaluation systems, limited calibration data, and a lack of a sub-watershed (i.e., township) scale. By applying multi-source data, this study expands the spatial scale of WRCC assessment and establishes a multidimensional evaluation framework for the water resources in dryland agriculture. The results indicate water stress index ranges from 0.52 to 1.67, and the comprehensive index of WRCC ranges from 0.25 to 0.70, which are significantly different in different types of irrigation areas and townships. Water quantity and water management are key factors influencing WRCC, the water ecosystem is an area requiring improvement, and the water environment is not a current constraint. Different irrigation areas and different types of townships should implement targeted measures to improve WRCC. Full article

(This article belongs to the Special Issue Precision Water Management in Dryland Agriculture)

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26 pages, 3267 KiB

Open AccessEditor’s ChoiceArticle

Multi-Chain Collaboration-Based Information Management and Control for the Rice Supply Chain

by Xiangzhen Peng, Xin Zhang, Xiaoyi Wang, Haisheng Li, Jiping Xu and Zhiyao Zhao

Agriculture 2022, 12(5), 689; https://doi.org/10.3390/agriculture12050689 - 12 May 2022

Cited by 21 | Viewed by 3642

Abstract

The issue of food quality and safety is a major concern. Rice is considered one of the three staple foods. Rice quality and safety problems have occurred frequently, which seriously affect human health. The rice supply chain is characterized by complex links, discrete [...] Read more.

The issue of food quality and safety is a major concern. Rice is considered one of the three staple foods. Rice quality and safety problems have occurred frequently, which seriously affect human health. The rice supply chain is characterized by complex links, discrete data, and numerous types of hazardous substances. Strengthening the information management and control capabilities of the rice supply chain is an important means to ensure the quality and safety of rice. Based on multi-chain collaboration, we have conducted research on information management and control of the rice supply chain. First, a multi-chain collaborative model of “blockchain + sub-chain” is designed. Based on this model, the following four mechanisms are designed: a trusted chain mechanism, a multi-level sub-chain encryption mechanism, a trusted supervision mechanism, and a hierarchical consensus mechanism. These mechanisms jointly serve the multi-chain collaborative management and control of the rice supply chain information. Secondly, smart contracts and operating procedures are designed, and a comparative analysis of them is executed. Finally, the design and implementation of the prototype system is carried out, and an example is verified and analyzed in a grain enterprise. Results show that this model serves the information supervision of the rice supply chain by studying the multi-chain collaboration. The study solves the real-time data interaction problem between each link of the rice supply chain. The credible management of information and control of the rice supply chain is accomplished. This study applies new information technology to the coordination and resource sharing of the food supply chain and provides ideas for the digital transformation of the food industry. Full article

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14 pages, 3774 KiB

Open AccessEditor’s ChoiceArticle

Parameters Optimization and Test of an Arc-Shaped Nail-Tooth Roller-Type Recovery Machine for Sowing Layer Residual Film

by Zhiyuan Zhang, Jingbin Li, Xianfei Wang, Yongman Zhao, Shuaikang Xue and Zipeng Su

Agriculture 2022, 12(5), 660; https://doi.org/10.3390/agriculture12050660 - 03 May 2022

Cited by 12 | Viewed by 2017

Abstract

The aim of this paper is to optimize the working parameters of the arc-shaped nail-tooth roller-type recovery machine for sowing layer residual film. Firstly, the tooth roller device of the residual film recovery machine is designed, and the main working parameters affecting the [...] Read more.

The aim of this paper is to optimize the working parameters of the arc-shaped nail-tooth roller-type recovery machine for sowing layer residual film. Firstly, the tooth roller device of the residual film recovery machine is designed, and the main working parameters affecting the operation of the machine and the value range of each parameter are determined through the analysis of the operation process. Secondly, virtual simulation technology is used to establish a virtual simulation model of the interaction process between the tooth roller device and soil. At the same time, taking the soil-hilling quantity as the index, we build a quadratic regression mathematical model with three factors—the forward speed, rotation speed, and working depth—using the Box–Behnken method. Consequently, the analysis of the simulation results show that the order of the most significant factors is working depth, rotation speed, and forward speed. The optimal combination of working parameters are as follows: a forward speed of 4.5 km/h, a rotation speed of 43.2 r/min, and a working depth of 100.0 mm. Meanwhile, the predicted value of the soil-hilling quantity is 23.1 kg. Finally, we carried out field tests using the optimal combination parameters; the results show that the normal residual film collection rate is 66.8%, the soil-hilling quantity is 24.2 kg, and the relative error between the test value and the predicted value is 4.8%. This indicates that the devised DEM simulation model can be used to predict the operational performance of the tooth roller device in the working process. This study provides a reference that can be used in the planning and boundary enhancement of agricultural machinery and equipment. Full article

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

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12 pages, 1716 KiB

Open AccessEditor’s ChoiceCommunication

Stomatal Regulation and Osmotic Adjustment in Sorghum in Response to Salinity

by Pablo Rugero Magalhães Dourado, Edivan Rodrigues de Souza, Monaliza Alves dos Santos, Cintia Maria Teixeira Lins, Danilo Rodrigues Monteiro, Martha Katharinne Silva Souza Paulino and Bruce Schaffer

Agriculture 2022, 12(5), 658; https://doi.org/10.3390/agriculture12050658 - 02 May 2022

Cited by 17 | Viewed by 2528

Abstract

Sorghum bicolor (L.) Moench, one of the most important dryland cereal crops, is moderately tolerant of soil salinity, a rapidly increasing agricultural problem due to inappropriate irrigation management and salt water intrusion into crop lands as a result of climate change. The mechanisms [...] Read more.

Sorghum bicolor (L.) Moench, one of the most important dryland cereal crops, is moderately tolerant of soil salinity, a rapidly increasing agricultural problem due to inappropriate irrigation management and salt water intrusion into crop lands as a result of climate change. The mechanisms for sorghum’s tolerance of high soil salinity have not been elucidated. This study tested whether sorghum plants adapt to salinity stress via stomatal regulation or osmotic adjustment. Sorghum plants were treated with one of seven concentrations of NaCl (0, 20, 40, 60, 80, or 100 mM). Leaf gas exchange (net CO₂ assimilation (A), transpiration (Tr); stomatal conductance of water vapor (gs), intrinsic water use efficiency (WUE)), and water (Ψw), osmotic (Ψo), and turgor Ψt potentials were evaluated at 40 days after the imposition of salinity treatments. Plants exhibited decreased A, gs, and Tr with increasing salinity, whereas WUE was not affected by NaCl treatment. Additionally, plants exhibited osmotic adjustment to increasing salinity. Thus, sorghum appears to adapt to high soil salinity via both osmotic adjustment and stomatal regulation. Full article

(This article belongs to the Special Issue Biosaline Agriculture and Salt Tolerance of Plants)

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