Showing papers in "Agriculture in 2021"
TL;DR: This work reviewed the latest CNN networks pertinent to plant leaf disease classification and summarized DL principles involved in plant disease classification, and summarized the main problems and corresponding solutions of CNN used for plant disease classified.
Abstract: Crop production can be greatly reduced due to various diseases, which seriously endangers food security. Thus, detecting plant diseases accurately is necessary and urgent. Traditional classification methods, such as naked-eye observation and laboratory tests, have many limitations, such as being time consuming and subjective. Currently, deep learning (DL) methods, especially those based on convolutional neural network (CNN), have gained widespread application in plant disease classification. They have solved or partially solved the problems of traditional classification methods and represent state-of-the-art technology in this field. In this work, we reviewed the latest CNN networks pertinent to plant leaf disease classification. We summarized DL principles involved in plant disease classification. Additionally, we summarized the main problems and corresponding solutions of CNN used for plant disease classification. Furthermore, we discussed the future development direction in plant disease classification.
108 citations
TL;DR: In this paper, the authors focus on the important role performed by beneficial soil microorganisms as a cost-effective, nontoxic, and eco-friendly approach in the management of the rhizosphere to promote plant growth and yield.
Abstract: The world’s human population continues to increase, posing a significant challenge in ensuring food security, as soil nutrients and fertility are limited and decreasing with time. Thus, there is a need to increase agricultural productivity to meet the food demands of the growing population. A high level of dependence on chemical fertilizers as a means of increasing food production has damaged the ecological balance and human health and is becoming too expensive for many farmers to afford. The exploitation of beneficial soil microorganisms as a substitute for chemical fertilizers in the production of food is one potential solution to this conundrum. Microorganisms, such as plant growth-promoting rhizobacteria and mycorrhizal fungi, have demonstrated their ability in the formulation of biofertilizers in the agricultural sector, providing plants with nutrients required to enhance their growth, increase yield, manage abiotic and biotic stress, and prevent phytopathogens attack. Recently, beneficial soil microbes have been reported to produce some volatile organic compounds, which are beneficial to plants, and the amendment of these microbes with locally available organic materials and nanoparticles is currently used to formulate biofertilizers to increase plant productivity. This review focuses on the important role performed by beneficial soil microorganisms as a cost-effective, nontoxic, and eco-friendly approach in the management of the rhizosphere to promote plant growth and yield.
95 citations
TL;DR: In this paper, the main abiotic stressors were examined, namely drought, heat and salinity stress, focusing on the mechanisms involved in the most common vegetable crops responses, and the use of eco-sustainable cultural techniques, such as biostimulants, grafting and genomic sequencing techniques, to increase the quality of tomato crop under adverse environmental conditions are also presented.
Abstract: Environmental pollution, increasing CO2 atmospheric levels and the greenhouse effect are closely associated with the ongoing climate change and the extreme climatic events we are witnessing all over the Earth. Drought, high temperature and salinity are among the main environmental stresses that negatively affect the yield of numerous crops, challenging the world food safety. These effects are more profound in vegetable crops which are generally more susceptible to climate change than field or tree crops. The response to single or combined environmental stressors involves various changes in plant morphology and physiology or in molecular processes. Knowing the mechanisms behind these responses may help towards the creation of more tolerant genotypes in the long-term. However, the imediacy of the problem requires urgently short-term measures such as the use of eco-sustainable agricultural practices which can alleviate the negative effects of environmental pollution and allow vegetable crops to adapt to adverse climatic conditions. In this review, the main abiotic stressors were examined, namely drought, heat and salinity stress, focusing on the mechanisms involved in the most common vegetable crops responses. Moreover, the use of eco-sustainable cultural techniques, such as biostimulants, grafting and genomic sequencing techniques, to increase the quality of tomato crop under adverse environmental conditions are also presented.
71 citations
TL;DR: A deep convolutional neural network that integrates an attention mechanism, which can better adapt to the diagnosis of a variety of tomato leaf diseases is proposed, which provides a high-performance solution for crop diagnosis under the real agricultural environment.
Abstract: Crop disease diagnosis is of great significance to crop yield and agricultural production. Deep learning methods have become the main research direction to solve the diagnosis of crop diseases. This paper proposed a deep convolutional neural network that integrates an attention mechanism, which can better adapt to the diagnosis of a variety of tomato leaf diseases. The network structure mainly includes residual blocks and attention extraction modules. The model can accurately extract complex features of various diseases. Extensive comparative experiment results show that the proposed model achieves the average identification accuracy of 96.81% on the tomato leaf diseases dataset. It proves that the model has significant advantages in terms of network complexity and real-time performance compared with other models. Moreover, through the model comparison experiment on the grape leaf diseases public dataset, the proposed model also achieves better results, and the average identification accuracy of 99.24%. It is certified that add the attention module can more accurately extract the complex features of a variety of diseases and has fewer parameters. The proposed model provides a high-performance solution for crop diagnosis under the real agricultural environment.
71 citations
TL;DR: This review presents studies about the application of NPs to enhance the production of bioactive plant metabolites in ex vitro as in vitro, in addition to the effect of post-harvest by NPs application.
Abstract: Bioactive compounds (e.g., flavonoids, phenolics acids, alkaloids and carotenoids) are commercially-valued products, due to their wide array of applications in the medical, pharmacological, cosmetic, agriculture and food industry. A strategy applied to increase or enhancing bioactive compounds production in plants is controlled elicitation. In recent years, many researchers have studied the role of nanoparticles (NPs) as a novel elicitor for the biosynthesis of bioactive compounds shown that the NPs could affect the plant’s secondary metabolism in plant and culture systems. In this sense, recent studies have highlighted the potential applications of nanotechnology in crop production by improving the nutraceutical and nutritional quality of plants. In this review, we present studies about the application of NPs to enhance the production of bioactive plant metabolites. The aforementioned studies in ex vitro as in vitro, in addition to the effect of post-harvest by NPs application.
68 citations
TL;DR: RF and SVM algorithms are efficient and practical to use, and can be implemented easily for detecting weed from UAV images.
Abstract: This paper explores the potential of machine learning algorithms for weed and crop classification from UAV images. The identification of weeds in crops is a challenging task that has been addressed through orthomosaicing of images, feature extraction and labelling of images to train machine learning algorithms. In this paper, the performances of several machine learning algorithms, random forest (RF), support vector machine (SVM) and k-nearest neighbours (KNN), are analysed to detect weeds using UAV images collected from a chilli crop field located in Australia. The evaluation metrics used in the comparison of performance were accuracy, precision, recall, false positive rate and kappa coefficient. MATLAB is used for simulating the machine learning algorithms; and the achieved weed detection accuracies are 96% using RF, 94% using SVM and 63% using KNN. Based on this study, RF and SVM algorithms are efficient and practical to use, and can be implemented easily for detecting weed from UAV images.
65 citations
TL;DR: The proposed model is an ensemble of pre-trained DenseNet121, EfficientNetB7, and E efficientNet NoisyStudent, which aims to classify leaves of apple trees into one of the following categories: healthy, apple scab, apple cedar rust, and multiple diseases, using its images, and achieves a good performance on different metrics.
Abstract: The automatic detection of diseases in plants is necessary, as it reduces the tedious work of monitoring large farms and it will detect the disease at an early stage of its occurrence to minimize further degradation of plants. Besides the decline of plant health, a country’s economy is highly affected by this scenario due to lower production. The current approach to identify diseases by an expert is slow and non-optimal for large farms. Our proposed model is an ensemble of pre-trained DenseNet121, EfficientNetB7, and EfficientNet NoisyStudent, which aims to classify leaves of apple trees into one of the following categories: healthy, apple scab, apple cedar rust, and multiple diseases, using its images. Various Image Augmentation techniques are included in this research to increase the dataset size, and subsequentially, the model’s accuracy increases. Our proposed model achieves an accuracy of 96.25% on the validation dataset. The proposed model can identify leaves with multiple diseases with 90% accuracy. Our proposed model achieved a good performance on different metrics and can be deployed in the agricultural domain to identify plant health accurately and timely.
62 citations
TL;DR: In this article, the authors provide a comprehensive overview regarding opportunities and challenges of traditional and newly developed CSA practices in Ethiopia, such as integrated soil fertility management, water harvesting, and agroforestry.
Abstract: Agriculture is the backbone of the Ethiopian economy, and the agricultural sector is dominated by smallholder farming systems. The farming systems are facing constraints such as small land size, lack of resources, and increasing degradation of soil quality that hamper sustainable crop production and food security. The effects of climate change (e.g., frequent occurrence of extreme weather events) exacerbate these problems. Applying appropriate technologies like climate-smart agriculture (CSA) can help to resolve the constraints of smallholder farming systems. This paper provides a comprehensive overview regarding opportunities and challenges of traditional and newly developed CSA practices in Ethiopia, such as integrated soil fertility management, water harvesting, and agroforestry. These practices are commonly related to drought resilience, stability of crop yields, carbon sequestration, greenhouse gas mitigation, and higher household income. However, the adoption of the practices by smallholder farmers is often limited, mainly due to shortage of cropland, land tenure issues, lack of adequate knowledge about CSA, slow return on investments, and insufficient policy and implementation schemes. It is suggested that additional measures be developed and made available to help CSA practices become more prevalent in smallholder farming systems. The measures should include the utilization of degraded and marginal lands, improvement of the soil organic matter management, provision of capacity-building opportunities and financial support, as well as the development of specific policies for smallholder farming.
61 citations
TL;DR: Experimental results show that the favorable prediction accuracy, robustness, and generalization of the proposed BEDA method make it suitable to more precisely manage greenhouses.
Abstract: Smart agricultural greenhouses provide well-controlled conditions for crop cultivation but require accurate prediction of environmental factors to ensure ideal crop growth and management efficiency. Due to the limitations of existing predictors in dealing with massive, nonlinear, and dynamic temporal data, this study proposes a bidirectional self-attentive encoder–decoder framework (BEDA) to construct the long-time predictor for multiple environmental factors with high nonlinearity and noise in a smart greenhouse. Firstly, the original data are denoised by wavelet threshold filter and pretreatment operations. Secondly, the bidirectional long short-term-memory is selected as the fundamental unit to extract time-serial features. Then, the multi-head self-attention mechanism is incorporated into the encoder–decoder framework to improve the prediction performance. Experimental investigations are conducted in a practical greenhouse to accurately predict indoor environmental factors (temperature, humidity, and CO2) from noisy IoT-based sensors. The best model for all datasets was the proposed BEDA method, with the root mean square error of three factors’ prediction reduced to 2.726, 3.621, and 49.817, and with an R of 0.749 for temperature, 0.848 for humidity, and 0.8711 for CO2 concentration, respectively. The experimental results show that the favorable prediction accuracy, robustness, and generalization of the proposed method make it suitable to more precisely manage greenhouses.
57 citations
TL;DR: In this article, the authors explored the key sources of carbon emissions within the agriculture sector and reviewed efficient ways to GHG emission via smart farming technology, which is effective at targeting inputs to the fields, helping to lower greenhouse gas emissions.
Abstract: Agriculture is an important source of greenhouse gas emissions. It is one of the economic sectors that impacts both directly and indirectly towards climate change which contributes to greenhouse gas emissions. There has been a continuous trend of agricultural greenhouse gas emissions reduction technologies, but any step taken in this direction must not negatively affect farm productivity and economics. For the agriculture sector to achieve reduced GHG emission, climate-smart activities and improved food security will be needed for this sector to become a climate-smart landscape. Climate-smart technologies are effective at targeting inputs to the fields, helping to lower greenhouse gas emissions. This article explores the key sources of carbon emissions within the agriculture sector and reviews efficient ways to GHG emission via Smart Farming technology. Based on the public archive GHG datasets, we have found that livestock farming is the largest GHG emission sector among other agricultural sectors and responsible for 70% of the total emission. Besides, we also show that Queensland is the largest agricultural GHG contributor compared to other states and territories. The article also captures any possible sources within smart farming that may contribute to carbon emissions and suggest ways to reduce GHG emissions. Besides, an Australian-based best management practice approach is discussed to review the emissions reduction strategy based on climate-specific technology to help the farmers and other stakeholders take environmentally-friendly agricultural decisions.
55 citations
TL;DR: The application of biofertilizers improved shoot length, root length, number of branches, plant dry weight, leaf area index (LAI), chlorophyll content, and nutrient uptake of guar plants compared with the control plants, and resulted in an obvious increase in seed yield.
Abstract: Guar is an economically important legume crop that is used for gum production. The clean and sustainable production of guar, especially in newly reclaimed lands, requires biofertilizers that can reduce the use of mineral fertilizers, which have harmful effects on human health and the environment. The present study was conducted to investigate the effects of biofertilizers produced from Bradyrhizobium sp., Bacillus subtilis, and arbuscular mycorrhizal fungi (AMF), individually or in combinations, on microbial activity, and nutrients of the soils and the guar growth and seed quality and yield. The application of biofertilizers improved shoot length, root length, number of branches, plant dry weight, leaf area index (LAI), chlorophyll content, and nutrient uptake of guar plants compared with the control plants. Moreover, the application with biofertilizers resulted in an obvious increase in seed yield and has improved the total proteins, carbohydrates, fats, starch, and guaran contents in the seeds. Additionally, biofertilizer treatments have improved the soil microbial activity by increasing dehydrogenase, phosphatase, protease, and invertase enzymes. Soil inoculation with the optimized doses of biofertilizers saved about 25% of the chemical fertilizers required for the entire guar growth stages. Our results could serve as a practical strategy for further research into integrated plant-microbe interaction in agriculture.
TL;DR: In this article, the harmonic framework of the agricultural UAV is presented, and then it abundantly illustrates the methods and materials of the UAV, and finally, the article portrays the outcome.
Abstract: Presently in agriculture, there is much ample scope for drone and UAS (Unmanned Aircraft System) development. Because of their low cost and small size, these devices have the ability to help many developing countries with economic prosperity. The entire aggregation of financial investments in the agricultural area has increased appreciably in recent years. Sooth to say, agriculture remains a massive part of the world’s commercial growth, and due to some complications, the agriculture fields withstand massive losses. Pets and destructive insects seem to be the primary reasons for certain degenerative diseases. It minimizes the potential productivity of the crops. For increasing the quality of the plants, fertilizers and pesticides are appropriately applied. Using UAVs (Unmanned Aerial Vehicles) for spraying pesticides and fertilizing materials is an exuberant contraption. It adequately reduces the rate of health dilemma and the number of workers, which is quite an impressive landmark. Willing producers are also adopting UAVs in agriculture to soil and field analysis, seed sowing, lessen the time and costs correlated with crop scouting, and field mapping. It is rapid, and it can sensibly diminish a farmer’s workload, which is significantly a part of the agricultural revolution. This article aims to proportionally represent the concept of agricultural purposed UAV clear to the neophytes. First, this paper outlines the harmonic framework of the agricultural UAV, and then it abundantly illustrates the methods and materials. Finally, the article portrays the outcome.
TL;DR: In this article, the downstream processing steps and principles involved in producing cannabinoids from Cannabis sativa L (Hemp) biomass are shed light on the downstream processes and principles for large-scale extraction.
Abstract: Cannabis plant has long been execrated by law in different nations due to the psychoactive properties of only a few cannabinoids. Recent scientific advances coupled with growing public awareness of cannabinoids as a medical commodity drove legislation change and brought about a historic transition where the demand rose over ten-fold in less than five years. On the other hand, the technology required for cannabis processing and the extraction of the most valuable chemical compounds from the cannabis flower remains the bottleneck of processing technology. This paper sheds light on the downstream processing steps and principles involved in producing cannabinoids from Cannabis sativa L. (Hemp) biomass. By categorizing the extraction technology into seed and trichome, we examined and critiqued different pretreatment methods and technological options available for large-scale extraction in both categories. Solvent extraction methods being the main focus, the critical decision-making parameters in each stage, and the applicable current technologies in the field, were discussed. We further examined the factors affecting the cannabinoid transformation that changes the medical functionality of the final cannabinoid products. Based on the current trends, the extraction technologies are continuously being revised and enhanced, yet they still fail to keep up with market demands.
TL;DR: In this article, a review of the existing vegetation indices used in viticulture, which were calculated from imagery acquired by remote sensing platforms such as satellites, airplanes and UAVs, is presented.
Abstract: One factor of precision agriculture is remote sensing, through which we can monitor vegetation health and condition. Much research has been conducted in the field of remote sensing and agriculture analyzing the applications, while the reviews gather the research on this field and examine different scientific methodologies. This work aims to gather the existing vegetation indices used in viticulture, which were calculated from imagery acquired by remote sensing platforms such as satellites, airplanes and UAVs. In this review we present the vegetation indices, the applications of these and the spatial distribution of the research on viticulture from the early 2000s. A total of 143 publications on viticulture were reviewed; 113 of them had used remote sensing methods to calculate vegetation indices, while the rejected ones have used proximal sensing methods. The findings show that the most used vegetation index is NDVI, while the most frequently appearing applications are monitoring and estimating vines water stress and delineation of management zones. More than half of the publications use multitemporal analysis and UAVs as the most used among remote sensing platforms. Spain and Italy are the countries with the most publications on viticulture with one-third of the publications referring to regional scale whereas the others to site-specific/vineyard scale. This paper reviews more than 90 vegetation indices that are used in viticulture in various applications and research topics, and categorized them depending on their application and the spectral bands that they are using. To summarize, this review is a guide for the applications of remote sensing and vegetation indices in precision viticulture and vineyard assessment.
TL;DR: This research proposes two machine learning models for the prediction of food production using the adaptive network-based fuzzy inference system (ANFIS) and multilayer perceptron (MLP) methods to advance the prediction models.
Abstract: Advancing models for accurate estimation of food production is essential for policymaking and managing national plans of action for food security. This research proposes two machine learning models for the prediction of food production. The adaptive network-based fuzzy inference system (ANFIS) and multilayer perceptron (MLP) methods are used to advance the prediction models. In the present study, two variables of livestock production and agricultural production were considered as the source of food production. Three variables were used to evaluate livestock production, namely livestock yield, live animals, and animal slaughtered, and two variables were used to assess agricultural production, namely agricultural production yields and losses. Iran was selected as the case study of the current study. Therefore, time-series data related to livestock and agricultural productions in Iran from 1961 to 2017 have been collected from the FAOSTAT database. First, 70% of this data was used to train ANFIS and MLP, and the remaining 30% of the data was used to test the models. The results disclosed that the ANFIS model with generalized bell-shaped (Gbell) built-in membership functions has the lowest error level in predicting food production. The findings of this study provide a suitable tool for policymakers who can use this model and predict the future of food production to provide a proper plan for the future of food security and food supply for the next generations.
TL;DR: The relationship among plant root morphology and physiology, rhizosphere microorganisms, and nitrogen is reviewed, summarized, and prospected.
Abstract: Fertilization is an important practical measure in agricultural production. As an important nutrient element of plants, nitrogen (N) has a significant impact on the plant productivity and microbial function. Rhizosphere microorganisms affect plant growth and development, nitrogen uptake and utilization, and ecological adaptability. The interaction mechanism between plant and rhizosphere microorganisms is one of the hotspots in life science research and the key program of agricultural microorganism utilization. In this article, the relationship among plant root morphology and physiology, rhizosphere microorganisms, and nitrogen is reviewed, summarized, and prospected.
TL;DR: The chemical structure, biosynthetic pathway, plant distribution, and physiological role of anthocyanins in plants are elucidated and the potential implications for human health derived from the consumption of foods rich in these molecules are discussed.
Abstract: In the past century, plant biostimulants have been increasingly used in agriculture as innovative and sustainable practice. Plant biostimulants have been mainly investigated as potential agents able to mitigate abiotic stress. However, few information is available about their ability to influence fruit quality or change fruit phytochemical composition. In particular, very little is known about their effects on anthocyanin synthesis and accumulation. Due to the increasing demand of consumers for healthier foods with high nutraceutical values, this review tries to fill the gap between anthocyanin content and biostimulant application. Here, we elucidate the chemical structure, biosynthetic pathway, plant distribution, and physiological role of anthocyanins in plants. Moreover, we discuss the potential implications for human health derived from the consumption of foods rich in these molecules. Finally, we report on literature data concerning the changes in anthocyanin content and profile after the application of biostimulant products on the most common anthocyanin-containing foods.
TL;DR: In this article, the use of a commercial seaweed biostimulant as an eco-friendly product (formally named True Algae Max (TAM)) was investigated for increasing cucumber yield under greenhouse conditions.
Abstract: Seaweed extract biostimulants are among the best modern sustainable biological plant growth promoters. They have been proven to eliminate plant diseases and abiotic stresses, leading to maximizing yields. Additionally, they have been listed as environmentally friendly biofertilizers. The focus of the present research is the use of a commercial seaweed biostimulant as an eco-friendly product (formally named True Algae Max (TAM). During the 2017 and 2018 seasons, five treatments of various NPK:TAM ratios were applied via regular fertigation, namely a conventional treatment of 100% NPK (C0) alongside combinations of 25%, 50%, 75%, and 100% (C25, C50, C75, and C100) of TAM, to evaluate the effectiveness of its bioactive compounds on enhancing growth, yield, and NPK content of cucumber (Cucumis sativus) under greenhouse conditions. TAM is rich in phytochemical compounds, such as milbemycin oxime, rhodopin, nonadecane, and 5-silaspiro [4.4]nona-1,3,6,8-tetraene,3,8-bis(diethylboryl)-2,7-diethyl-1,4,6,9-tetraphenyl-. Promising measured parameter outcomes showed the potentiality of applying TAM with and without mixes of ordinary NPK application. TAM could increase cucumber yield due to improving chemical and physical features related to immunity, productivity, and stress defense. In conclusion, it is better to avoid applying mineral fertilizers, considering also that the organic agricultural and welfare sectors could shortly depend on such biotechnological tools and use them to fulfill global food demands for improved sustainability.
TL;DR: Sulphur plays crucial roles in plant growth and development, with its functions ranging from being a structural constituent of macro-biomolecules to modulating several physiological processes and tolerance to abiotic stresses as mentioned in this paper.
Abstract: Sulphur plays crucial roles in plant growth and development, with its functions ranging from being a structural constituent of macro-biomolecules to modulating several physiological processes and tolerance to abiotic stresses. In spite of these numerous sulphur roles being well acknowledged, agriculture has paid scant regard for sulphur nutrition, until only recently. Serious problems related to soil sulphur deficiencies have emerged and the intensification of food, fiber, and animal production is escalating to feed the ever-increasing human population. In the wake of huge demand for high quality cereal and vegetable diets, sulphur can play a key role in augmenting the production, productivity, and quality of crops. Additionally, in light of the emerging problems of soil fertility exhaustion and climate change-exacerbated environmental stresses, sulphur assumes special importance in crop production, particularly under intensively cropped areas. Here, citing several relevant examples, we highlight, in addition to its plant biological and metabolism functions, how sulphur can significantly enhance crop productivity and quality, as well as acclimation to abiotic stresses. By this appraisal, we also aim to stimulate readers interests in crop sulphur research by providing priorities for future pursuance, including bettering our understanding of the molecular processes and dynamics of sulphur availability and utilization in plants, dissecting the role of soil rhizospherical microbes in plant sulphur transformations, enhancing plant phenotyping and diagnosis for nutrient deficiencies, and matching site-specific crop sulphur demands with fertilizer amendments in order to reduce nutrient use inefficiencies in both crop and livestock production systems. This will facilitate the proper utilization of sulphur in crop production and eventually enhance sustainable and environmentally friend food production.
TL;DR: This paper reviewed the application research status of DEM in two aspects: first is the DEM model establishment of common agricultural materials such as soil, crop seed, and straw, etc, and the simulation of typical operational processes of agricultural machines or their components.
Abstract: As a promising and convenient numerical calculation approach, the discrete element method (DEM) has been increasingly adopted in the research of agricultural machinery. DEM is capable of monitoring and recording the dynamic and mechanical behavior of agricultural materials in the operational process of agricultural machinery, from both a macro-perspective and micro-perspective; which has been a tremendous help for the design and optimization of agricultural machines and their components. This paper reviewed the application research status of DEM in two aspects: First is the DEM model establishment of common agricultural materials such as soil, crop seed, and straw, etc. The other is the simulation of typical operational processes of agricultural machines or their components, such as rotary tillage, subsoiling, soil compaction, furrow opening, seed and fertilizer metering, crop harvesting, and so on. Finally, we evaluate the development prospects of the application of research on the DEM in agricultural machinery, and look forward to promoting its application in the field of the optimization and design of agricultural machinery.
TL;DR: In this paper, the authors present a review of the benefits and costs of improving the welfare of farm animals and highlight the need for further empirical evidence to improve decision-making in animal welfare.
Abstract: It costs money to improve the welfare of farm animals. For people with animals under their care, there are many factors to consider regarding changes in practice to improve welfare, and the optimal course of action is not always obvious. Decision support systems for animal welfare, such as economic cost–benefit analyses, are lacking. This review attempts to provide clarity around the costs and benefits of improving farm animal welfare, thereby enabling the people with animals under their care to make informed decisions. Many of the costs are obvious. For example, training of stockpeople, reconfiguration of pens, and administration of pain relief can improve welfare, and all incur costs. Other costs are less obvious. For instance, there may be substantial risks to market protection, consumer acceptance, and social licence to farm associated with not ensuring good animal welfare. The benefits of improving farm animal welfare are also difficult to evaluate from a purely economic perspective. Although it is widely recognised that animals with poor welfare are unlikely to produce at optimal levels, there may be benefits of improving animal welfare that extend beyond production gains. These include benefits to the animal, positive effects on the workforce, competitive advantage for businesses, mitigation of risk, and positive social consequences. We summarise these considerations into a decision tool that can assist people with farm animals under their care, and we highlight the need for further empirical evidence to improve decision-making in animal welfare.
TL;DR: In this paper, the effects of nitrogen and phosphorus levels on the physiological traits, yield, and seed yield of rapeseed (Brassica napus L.), were studied in a farm research project of Zanjan University.
Abstract: The effects of nitrogen and phosphorus levels on the physiological traits, yield, and seed yield of rapeseed (Brassica napus L.), were studied in a farm research project of Zanjan University. Three levels of nitrogen (0, 100, and 200 kg/ha) and three levels of phosphorus (0, 75, and 150 kg/ha) were considered. The results showed that an increase in nitrogen level caused an increase in the leaf chlorophyll content so that the application of 200 kg/ha of nitrogen increased the chlorophyll content of the leaves until the mid-grain filling stage. Nitrogen application lowered leaf stomatal conductance in the early flowering stage whereas the stomatal conductance was increased during the late flowering stage. Nitrogen application (100 and 200 kg/ha) also increased the quantum yield of photosystem II. On the other hand, with the application of 150 kg/ha and 75 kg/ha of phosphorus, the leaf stomatal conductance and the quantum yield of photosystem II in the early flowering stage increased respectively. The results showed that the application of 200 kg/ha of nitrogen and 75 kg/ha of phosphorus significantly increased seed and oil yield compared to the control. In addition, the number of siliques per plant and the weight of 1000 seeds showed an increasing trend that was affected by nitrogen and phosphorus levels. This study demonstrated that nitrogen enhanced the chlorophyll content, leaf area, and consequently, the quantum yield of photosystem II. Nitrogen also augmented the seed filling duration, seed yield, and oil yield by increasing gas exchange. As a result, the application of 100 kg/ha of nitrogen together with 75 kg/ha phosphorus showed the greatest effect on the qualitative and quantitative yield of rapeseed. However, the application of 200 kg/ha of nitrogen alone or in combination with different levels of phosphorus did not significantly increase many of the studied traits.
TL;DR: Water deficit is one of the most problematic stressors worldwide and the use of biostimulants represents an increasingly ecological practice aimed to improve crop tolerance and mitigate the negative effects on the productivity.
Abstract: Water deficit is one of the most problematic stressors worldwide. In this context, the use of biostimulants represents an increasingly ecological practice aimed to improve crop tolerance and mitigate the negative effects on the productivity. Here, the effect derived from the foliar application of ERANTHIS®®, a biostimulant based on seaweed (Ascophyllum nodosum and Laminaria digitata) and yeast extracts, was tested on tomato plants grown under mild water-stress conditions. The potential stress mitigation action was evaluated by monitoring morphometric (fresh weight and dry matter content), physiological (stem water potential) and biochemical (ROS scavenger enzymes activity, proline, abscisic acid, hydrogen peroxide and photosynthetic pigment content) parameters closely related to the occurrence and response to stress at both flowering and fruit-set timing. In general, we observed that plants grown under drought conditions and treated with the biostimulant had a lower amount of ABA, and MDA and proline correlated to a lower activity of ROS scavenger enzymes compared to untreated plants. These data, together with the higher stem water potential and photosynthetic pigment levels recorded for the treated plants, suggest that ERANTHIS®® may mitigate water stress effects on tomato.
TL;DR: High doses of triazole fungicides strongly affects the structure of the microbial communities in soil and usually decrease the soil microbial population and the activities of enzymes found in soil.
Abstract: Triazole fungicides can manifest toxicity to a wide range of non-target organisms. Within this study we present a systematic review of the effects produced on the soil microbiota and activity of soil enzymes by the following triazole fungicides: cyproconazole, difenoconazole, epoxiconazole, flutriafol, hexaconazole, metconazole, myclobutanil, paclobutrazole, propiconazole, tebuconazole, tetraconazole, triadimenol, triadimefon, and triticonazole. Known effects of the triazole fungicides on the soil activity are dose dependent. High doses of triazole fungicides strongly affects the structure of the microbial communities in soil and usually decrease the soil microbial population and the activities of enzymes found in soil.
TL;DR: In this article, an alternative method for oil palm tree management is proposed by applying high-resolution imagery, combined with Faster-RCNN, for automatic detection and health classification of oil palm trees.
Abstract: Combining modern technology and agriculture is an important consideration for the effective management of oil palm trees. In this study, an alternative method for oil palm tree management is proposed by applying high-resolution imagery, combined with Faster-RCNN, for automatic detection and health classification of oil palm trees. This study used a total of 4172 bounding boxes of healthy and unhealthy palm trees, constructed from 2000 pixel × 2000 pixel images. Of the total dataset, 90% was used for training and 10% was prepared for testing using Resnet-50 and VGG-16. Three techniques were used to assess the models’ performance: model training evaluation, evaluation using visual interpretation, and ground sampling inspections. The study identified three characteristics needed for detection and health classification: crown size, color, and density. The optimal altitude to capture images for detection and classification was determined to be 100 m, although the model showed satisfactory performance up to 140 m. For oil palm tree detection, healthy tree identification, and unhealthy tree identification, Resnet-50 obtained F1-scores of 95.09%, 92.07%, and 86.96%, respectively, with respect to visual interpretation ground truth and 97.67%, 95.30%, and 57.14%, respectively, with respect to ground sampling inspection ground truth. Resnet-50 yielded better F1-scores than VGG-16 in both evaluations. Therefore, the proposed method is well suited for the effective management of crops.
TL;DR: In this article, the impact of climate change on rice yields in Malaysia was examined using a panel data approach using data from 1987 to 2017 on eight granary areas in Peninsular Malaysia.
Abstract: Climate change is a global problem since many countries worldwide are becoming increasingly vulnerable to natural disasters. Numerous climate models in various studies project a decline in agricultural productivity that will mainly be due to excessive heat in tropical and subtropical regions, especially in Southeast Asia. As a Southeast Asian country, Malaysia is no exception to this problem. Hence, the present study aimed to examine the impact of climate change on rice yields in Malaysia. A panel data approach was adopted using data from 1987 to 2017 on eight granary areas in Peninsular Malaysia. The main objectives were to assess the impact of climate variables (i.e., minimum and maximum temperature and precipitation) on rice yield and the variance of the impact during the main season and off-season. Our regression results indicate that precipitation was not statistically significant in all model specifications for both the main and off-season. While the maximum temperature was found to be negatively associated with yield during the off-season, the minimum temperature showed a positive effect in both cropping seasons. We used the HadGEM3-GC31 N512 resolution model based on the high-emission Shared Socioeconomic Pathways 8.5 scenario (SSPs-8.5) from the High-Resolution Model Intercomparison Project (HighResMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) to project future climate change in 2030 and 2040. The projected results indicate that rice yield would show a more positive trend by 2040 when compared to the previous decade, ranging from −0.02 to 19.85% during the main season and −2.77 to 7.41% during the off-season. Although rice yield is likely to increase in certain areas, other areas are projected to experience negative effects. Hence, adaptation at the farm level remains crucial, specifically during the off-season, since climate change could widen the gaps in rice yields between cropping seasons and among granary areas.
TL;DR: In this paper, the authors identified the behavior of Polish consumers in the market of organic products and established a link between their environmental awareness and willingness to buy organic products by using a proprietary survey questionnaire.
Abstract: The dynamically developing trend of sustainable consumption is manifested, among others, by the growing interest in organic products on the part of consumers The aim of this article was to identify the behavior of Polish consumers in the market of organic products and to establish a link between their environmental awareness and willingness to buy organic products The authors hypothesized that there is a relationship between consumer awareness of the concept of sustainable consumption and the consumption of organic products Consumer awareness means making conscious choices based on the knowledge expressed in the attitudes and, sometimes, preferences of the food brand The research was conducted using a proprietary survey questionnaire A total of 1067 respondents participated A statistical analysis was performed by using Statistica 131 PL software, which includes descriptive statistics, the discriminant function analysis, and regression analysis Motives were identified that are of crucial importance to the consumer deciding to purchase organic products These include: beneficial health effects, contents of nutrients, no additional substances used in food production, taste, and others A statistical relationship was established between environmental awareness and the tendency to buy organic products Among the organic products, eggs, fresh fruit and vegetables, honey, cow’s milk and its derivatives, as well as cereal products, are the most preferred by consumers of both genders The proposed model, which outlines the relationship between environmental awareness and the tendency to buy organic products, includes the following variables: care for the environment and animal welfare, no harmful substances used in food production, low level of processing, short shelf life
TL;DR: The purpose of this Special Issue was to publish high-quality research and review papers that cover the application of various types of artificial neural networks in solving relevant tasks and problems of widely defined agriculture.
Abstract: Artificial neural networks are one of the most important elements of machine learning and artificial intelligence. They are inspired by the human brain structure and function as if they are based on interconnected nodes in which simple processing operations take place. The spectrum of neural networks application is very wide, and it also includes agriculture. Artificial neural networks are increasingly used by food producers at every stage of agricultural production and in efficient farm management. Examples of their applications include: forecasting of production effects in agriculture on the basis of a wide range of independent variables, verification of diseases and pests, intelligent weed control, and classification of the quality of harvested crops. Artificial intelligence methods support decision-making systems in agriculture, help optimize storage and transport processes, and make it possible to predict the costs incurred depending on the chosen direction of management. The inclusion of machine learning methods in the “life cycle of a farm” requires handling large amounts of data collected during the entire growing season and having the appropriate software. Currently, the visible development of precision farming and digital agriculture is causing more and more farms to turn to tools based on artificial intelligence. The purpose of this Special Issue was to publish high-quality research and review papers that cover the application of various types of artificial neural networks in solving relevant tasks and problems of widely defined agriculture.
TL;DR: In this article, the authors highlight the global prevalence of salinity and sodicity in irrigated areas, highlight their spatiotemporal variability and causes, document the effects of irrigation induced salinity on physicochemical properties of soil and groundwater, and discuss practical, innovative, and feasible practices and solutions to mitigate the Salinity and Sodicity hazards.
Abstract: Salinity and sodicity have been a major environmental hazard of the past century since more than 25% of the total land and 33% of the irrigated land globally are affected by salinity and sodicity. Adverse effects of soil salinity and sodicity include inhibited crop growth, waterlogging issues, groundwater contamination, loss in soil fertility and other associated secondary impacts on dependent ecosystems. Salinity and sodicity also have an enormous impact on food security since a substantial portion of the world’s irrigated land is affected by them. While the intrinsic nature of the soil could cause soil salinity and sodicity, in developing countries, they are also primarily caused by unsustainable irrigation practices, such as using high volumes of fertilizers, irrigating with saline/sodic water and lack of adequate drainage facilities to drain surplus irrigated water. This has also caused irreversible groundwater contamination in many regions. Although several remediation techniques have been developed, comprehensive land reclamation still remains challenging and is often time and resource inefficient. Mitigating the risk of salinity and sodicity while continuing to irrigate the land, for example, by growing salt-resistant crops such as halophytes together with regular crops or creating artificial drainage appears to be the most practical solution as farmers cannot halt irrigation. The purpose of this review is to highlight the global prevalence of salinity and sodicity in irrigated areas, highlight their spatiotemporal variability and causes, document the effects of irrigation induced salinity and sodicity on physicochemical properties of soil and groundwater, and discuss practical, innovative, and feasible practices and solutions to mitigate the salinity and sodicity hazards on soil and groundwater.
TL;DR: In this article, the authors examined the impact of transport infrastructure on the sustainable socio-economic development of the Walcz Lake District in the Czech Republic, using a questionnaire addressed to entrepreneurs from this region.
Abstract: The transport infrastructure can be defined as a factor that guarantees the growth and economic development of the region, due to the functions of traversing space in terms of the movement of people and the exchange of goods. The effects of the impact of transport infrastructure on the economy of the region largely depend on how the society uses the services offered by infrastructure facilities and devices. The study examines the impact of transport infrastructure on the sustainable socio-economic development of the Walcz Lake District. To conduct the analysis, a questionnaire addressed to entrepreneurs from this region was used. In the second part of the research, the indicators of sustainable development at the regional level were applied: the level of transport infrastructure and the level of socio-economic development of the studied area. The study is an attempt to fill the cognitive gap for areas outside the country’s main transport corridors. The existing differentiation in both the development of infrastructure and the economic attractiveness of urban and rural areas was shown. Factors influencing the effectiveness of implementing the concept of sustainable rural development were indicated.