Hasil untuk "Agriculture"

R. Sishodia, Ram Lakhan Ray, Sudhir Kumar Singh

Agriculture provides for the most basic needs of humankind: food and fiber. The introduction of new farming techniques in the past century (e.g., during the Green Revolution) has helped agriculture keep pace with growing demands for food and other agricultural products. However, further increases in food demand, a growing population, and rising income levels are likely to put additional strain on natural resources. With growing recognition of the negative impacts of agriculture on the environment, new techniques and approaches should be able to meet future food demands while maintaining or reducing the environmental footprint of agriculture. Emerging technologies, such as geospatial technologies, Internet of Things (IoT), Big Data analysis, and artificial intelligence (AI), could be utilized to make informed management decisions aimed to increase crop production. Precision agriculture (PA) entails the application of a suite of such technologies to optimize agricultural inputs to increase agricultural production and reduce input losses. Use of remote sensing technologies for PA has increased rapidly during the past few decades. The unprecedented availability of high resolution (spatial, spectral and temporal) satellite images has promoted the use of remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient application, disease and pest management, and yield prediction. In this paper, we provide an overview of remote sensing systems, techniques, and vegetation indices along with their recent (2015–2020) applications in PA. Remote-sensing-based PA technologies such as variable fertilizer rate application technology in Green Seeker and Crop Circle have already been incorporated in commercial agriculture. Use of unmanned aerial vehicles (UAVs) has increased tremendously during the last decade due to their cost-effectiveness and flexibility in obtaining the high-resolution (cm-scale) images needed for PA applications. At the same time, the availability of a large amount of satellite data has prompted researchers to explore advanced data storage and processing techniques such as cloud computing and machine learning. Given the complexity of image processing and the amount of technical knowledge and expertise needed, it is critical to explore and develop a simple yet reliable workflow for the real-time application of remote sensing in PA. Development of accurate yet easy to use, user-friendly systems is likely to result in broader adoption of remote sensing technologies in commercial and non-commercial PA applications.

L. Klerkx, E. Jakku, P. Labarthe

Abstract While there is a lot of literature from a natural or technical sciences perspective on different forms of digitalization in agriculture (big data, internet of things, augmented reality, robotics, sensors, 3D printing, system integration, ubiquitous connectivity, artificial intelligence, digital twins, and blockchain among others), social science researchers have recently started investigating different aspects of digital agriculture in relation to farm production systems, value chains and food systems. This has led to a burgeoning but scattered social science body of literature. There is hence lack of overview of how this field of study is developing, and what are established, emerging, and new themes and topics. This is where this article aims to make a contribution, beyond introducing this special issue which presents seventeen articles dealing with social, economic and institutional dynamics of precision farming, digital agriculture, smart farming or agriculture 4.0. An exploratory literature review shows that five thematic clusters of extant social science literature on digitalization in agriculture can be identified: 1) Adoption, uses and adaptation of digital technologies on farm; 2) Effects of digitalization on farmer identity, farmer skills, and farm work; 3) Power, ownership, privacy and ethics in digitalizing agricultural production systems and value chains; 4) Digitalization and agricultural knowledge and innovation systems (AKIS); and 5) Economics and management of digitalized agricultural production systems and value chains. The main contributions of the special issue articles are mapped against these thematic clusters, revealing new insights on the link between digital agriculture and farm diversity, new economic, business and institutional arrangements both on-farm, in the value chain and food system, and in the innovation system, and emerging ways to ethically govern digital agriculture. Emerging lines of social science enquiry within these thematic clusters are identified and new lines are suggested to create a future research agenda on digital agriculture, smart farming and agriculture 4.0. Also, four potential new thematic social science clusters are also identified, which so far seem weakly developed: 1) Digital agriculture socio-cyber-physical-ecological systems conceptualizations; 2) Digital agriculture policy processes; 3) Digitally enabled agricultural transition pathways; and 4) Global geography of digital agriculture development. This future research agenda provides ample scope for future interdisciplinary and transdisciplinary science on precision farming, digital agriculture, smart farming and agriculture 4.0.

Rachel Backer, J. Rokem, Gayathri Ilangumaran et al.

Microbes of the phytomicrobiome are associated with every plant tissue and, in combination with the plant form the holobiont. Plants regulate the composition and activity of their associated bacterial community carefully. These microbes provide a wide range of services and benefits to the plant; in return, the plant provides the microbial community with reduced carbon and other metabolites. Soils are generally a moist environment, rich in reduced carbon which supports extensive soil microbial communities. The rhizomicrobiome is of great importance to agriculture owing to the rich diversity of root exudates and plant cell debris that attract diverse and unique patterns of microbial colonization. Microbes of the rhizomicrobiome play key roles in nutrient acquisition and assimilation, improved soil texture, secreting, and modulating extracellular molecules such as hormones, secondary metabolites, antibiotics, and various signal compounds, all leading to enhancement of plant growth. The microbes and compounds they secrete constitute valuable biostimulants and play pivotal roles in modulating plant stress responses. Research has demonstrated that inoculating plants with plant-growth promoting rhizobacteria (PGPR) or treating plants with microbe-to-plant signal compounds can be an effective strategy to stimulate crop growth. Furthermore, these strategies can improve crop tolerance for the abiotic stresses (e.g., drought, heat, and salinity) likely to become more frequent as climate change conditions continue to develop. This discovery has resulted in multifunctional PGPR-based formulations for commercial agriculture, to minimize the use of synthetic fertilizers and agrochemicals. This review is an update about the role of PGPR in agriculture, from their collection to commercialization as low-cost commercial agricultural inputs. First, we introduce the concept and role of the phytomicrobiome and the agricultural context underlying food security in the 21st century. Next, mechanisms of plant growth promotion by PGPR are discussed, including signal exchange between plant roots and PGPR and how these relationships modulate plant abiotic stress responses via induced systemic resistance. On the application side, strategies are discussed to improve rhizosphere colonization by PGPR inoculants. The final sections of the paper describe the applications of PGPR in 21st century agriculture and the roadmap to commercialization of a PGPR-based technology.

A. Kamilaris, Agusti Fonts, F. Prenafeta-Boldú

Abstract Blockchain is an emerging digital technology allowing ubiquitous financial transactions among distributed untrusted parties, without the need of intermediaries such as banks. This article examines the impact of blockchain technology in agriculture and food supply chain, presents existing ongoing projects and initiatives, and discusses overall implications, challenges and potential, with a critical view over the maturity of these projects. Our findings indicate that blockchain is a promising technology towards a transparent supply chain of food, with many ongoing initiatives in various food products and food-related issues, but many barriers and challenges still exist, which hinder its wider popularity among farmers and systems. These challenges involve technical aspects, education, policies and regulatory frameworks.

Abhinav Sharma, Arpit Jain, Prateek Gupta et al.

Agriculture plays a vital role in the economic growth of any country. With the increase of population, frequent changes in climatic conditions and limited resources, it becomes a challenging task to fulfil the food requirement of the present population. Precision agriculture also known as smart farming have emerged as an innovative tool to address current challenges in agricultural sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives the machine ability to learn without being explicitly programmed. ML together with IoT (Internet of Things) enabled farm machinery are key components of the next agriculture revolution. In this article, authors present a systematic review of ML applications in the field of agriculture. The areas that are focused are prediction of soil parameters such as organic carbon and moisture content, crop yield prediction, disease and weed detection in crops and species detection. ML with computer vision are reviewed for the classification of a different set of crop images in order to monitor the crop quality and yield assessment. This approach can be integrated for enhanced livestock production by predicting fertility patterns, diagnosing eating disorders, cattle behaviour based on ML models using data collected by collar sensors, etc. Intelligent irrigation which includes drip irrigation and intelligent harvesting techniques are also reviewed that reduces human labour to a great extent. This article demonstrates how knowledge-based agriculture can improve the sustainable productivity and quality of the product.

V. Sáiz-Rubio, F. Rovira-Más

The information that crops offer is turned into profitable decisions only when efficiently managed. Current advances in data management are making Smart Farming grow exponentially as data have become the key element in modern agriculture to help producers with critical decision-making. Valuable advantages appear with objective information acquired through sensors with the aim of maximizing productivity and sustainability. This kind of data-based managed farms rely on data that can increase efficiency by avoiding the misuse of resources and the pollution of the environment. Data-driven agriculture, with the help of robotic solutions incorporating artificial intelligent techniques, sets the grounds for the sustainable agriculture of the future. This paper reviews the current status of advanced farm management systems by revisiting each crucial step, from data acquisition in crop fields to variable rate applications, so that growers can make optimized decisions to save money while protecting the environment and transforming how food will be produced to sustainably match the forthcoming population growth.

S. Piao, P. Ciais, Yao Huang et al.

Zhaoyu Zhai, José-Fernán Martínez, Victoria Beltran et al.

Abstract Undoubtedly, high demands for food from the world-wide growing population are impacting the environment and putting many pressures on agricultural productivity. Agriculture 4.0, as the fourth evolution in the farming technology, puts forward four essential requirements: increasing productivity, allocating resources reasonably, adapting to climate change, and avoiding food waste. As advanced information systems and Internet technologies are adopted in Agriculture 4.0, enormous farming data, such as meteorological information, soil conditions, marketing demands, and land uses, can be collected, analyzed, and processed for assisting farmers in making appropriate decisions and obtaining higher profits. Therefore, agricultural decision support systems for Agriculture 4.0 has become a very attractive topic for the research community. The objective of this paper aims at exploring the upcoming challenges of employing agricultural decision support systems in Agriculture 4.0. Future researchers may improve the decision support systems by overcoming these detected challenges. In this paper, the systematic literature review technique is used to survey thirteen representative decision support systems, including their applications for agricultural mission planning, water resources management, climate change adaptation, and food waste control. Each decision support system is analyzed under a systematic manner. A comprehensive evaluation is conducted from the aspects of interoperability, scalability, accessibility, usability, etc. Based on the evaluation result, upcoming challenges are detected and summarized, suggesting the development trends and demonstrating potential improvements for future research.

Mario Lezoche, J. H. Hormazabal, M. M. E. A. Díaz et al.

Abstract The term “Agri-Food 4.0” is an analogy to the term "Industry 4.0", coming from the concept “agriculture 4.0”. Since the origins of the industrial revolution, where the steam engines started the concept of Industry 1.0 and later the use of electricity upgraded the concept to Industry 2.0, the use of technologies generated a milestone in the industry revolution by addressing the Industry 3.0 concept. Hence, Industry 4.0, it is about including and integrating the latest developments based on digital technologies as well as the interoperability process across them. This allows enterprises to transmit real-time information in terms behaviour and performance. Therefore, the challenge is to maintain these complex networked structures efficiently linked and organised within the use of such technologies, especially to identify and satisfy supply chain stakeholders dynamic requirements. In this context, the agriculture domain is not an exception although it possesses some specialities depending from the domain. In fact, all agricultural machinery incorporates electronic controls and has entered to the digital age, enhancing their current performance. In addition, electronics, using sensors and drones, support the data collection of several agriculture key aspects, such as weather, geographical spatialization, animals and crops behaviours, as well as the entire farm life cycle. However, the use of the right methods and methodologies for enhancing agriculture supply chains performance is still a challenge, thus the concept of Industry 4.0 has evolved and adapted to agriculture 4.0 in order analyse the behaviours and performance in this specific domain. Thus, the question mark on how agriculture 4.0 support a better supply chain decision-making process, or how can help to save time to farmer to make effective decision based on objective data, remains open. Therefore, in this survey, a review of more than hundred papers on new technologies and the new available supply chains methods are analysed and contrasted to understand the future paths of the Agri-Food domain.

Nicola Gallai, J. Salles, J. Settele et al.

There is mounting evidence of pollinator decline all over the world and consequences in many agricultural areas could be significant. We assessed these consequences by measuring 1) the contribution of insect pollination to the world agricultural output economic value, and 2) the vulnerability of world agriculture in the face of pollinator decline. We used a bioeconomic approach, which integrated the production dependence ratio on pollinators, for the 100 crops used directly for human food worldwide as listed by FAO. The total economic value of pollination worldwide amounted to €153 billion, which represented 9.5% of the value of the world agricultural production used for human food in 2005. In terms of welfare, the consumer surplus loss was estimated between €190 and €310 billion based upon average price elasticities of − 1.5 to − 0.8, respectively. Vegetables and fruits were the leading crop categories in value of insect pollination with about €50 billion each, followed by edible oil crops, stimulants, nuts and spices. The production value of a ton of the crop categories that do not depend on insect pollination averaged €151 while that of those that are pollinator-dependent averaged €761. The vulnerability ratio was calculated for each crop category at the regional and world scales as the ratio between the economic value of pollination and the current total crop value. This ratio varied considerably among crop categories and there was a positive correlation between the rate of vulnerability to pollinators decline of a crop category and its value per production unit. Looking at the capacity to nourish the world population after pollinator loss, the production of 3 crop categories - namely fruits, vegetables, and stimulants - will clearly be below the current consumption level at the world scale and even more so for certain regions like Europe. Yet, although our valuation clearly demonstrates the economic importance of insect pollinators, it cannot be considered as a scenario since it does not take into account the strategic responses of the markets.

Lefteris Benos, A. Tagarakis, Georgios Dolias et al.

The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

Abderahman Rejeb, A. Abdollahi, K. Rejeb et al.

Ye Liu, Xiaoyuan Ma, Lei Shu et al.

The three previous industrial revolutions profoundly transformed agriculture industry from indigenous farming to mechanized farming and recent precision agriculture. Industrial farming paradigm greatly improves productivity, but a number of challenges have gradually emerged, which have exacerbated in recent years. Industry 4.0 is expected to reshape the agriculture industry once again and promote the fourth agricultural revolution. In this article, first, we review the current status of industrial agriculture along with lessons learned from industrialized agricultural production patterns, industrialized agricultural production processes, and the industrialized agri-food supply chain. Furthermore, five emerging technologies, namely the Internet of Things, robotics, artificial intelligence, big data analytics, and blockchain, toward Agriculture 4.0 are discussed. Specifically, we focus on the key applications of these emerging technologies in the agricultural sector and corresponding research challenges. This article aims to open up new research opportunities for readers, particularly industrial practitioners.

L. Klerkx, D. Rose

Abstract Agriculture 4.0 is comprised of different already operational or developing technologies such as robotics, nanotechnology, synthetic protein, cellular agriculture, gene editing technology, artificial intelligence, blockchain, and machine learning, which may have pervasive effects on future agriculture and food systems and major transformative potential. These technologies underpin con­cepts such as ver­ti­cal farm­ing and food systems, dig­i­tal agri­cul­ture, bioe­con­omy, cir­cu­lar agri­cul­ture, and aquapon­ics. In this perspective paper, we argue that more attention is needed for the inclusion and exclusion effects of Agriculture 4.0 technologies, and for reflection on how they relate to diverse transition pathways towards sustainable agricultural and food systems driven by mission-oriented innovation systems. This would require processes of responsible innovation, anticipating the potential impacts of Agriculture 4.0 through inclusive processes, and reflecting on and being responsive to emerging effects and where needed adjusting the direction and course of transition pathways.

F. Pendrill, T. Gardner, P. Meyfroidt et al.

Tropical deforestation continues at alarming rates with profound impacts on ecosystems, climate, and livelihoods, prompting renewed commitments to halt its continuation. Although it is well established that agriculture is a dominant driver of deforestation, rates and mechanisms remain disputed and often lack a clear evidence base. We synthesize the best available pantropical evidence to provide clarity on how agriculture drives deforestation. Although most (90 to 99%) deforestation across the tropics 2011 to 2015 was driven by agriculture, only 45 to 65% of deforested land became productive agriculture within a few years. Therefore, ending deforestation likely requires combining measures to create deforestation-free supply chains with landscape governance interventions. We highlight key remaining evidence gaps including deforestation trends, commodity-specific land-use dynamics, and data from tropical dry forests and forests across Africa. Description Forest loss for food Agricultural expansion is recognized as a major driver of forest loss in the tropics. However, accurate data on the links between agriculture and tropical deforestation are lacking. Pendrill et al. synthesized existing research and datasets to quantify the extent to which tropical deforestation from 2011 to 2015 was associated with agriculture. They estimated that at least 90% of deforested land occurred in landscapes where agriculture drove forest loss, but only about half was converted into productive agricultural land. Data availability and trends vary across regions, suggesting complex links between agriculture and forest loss. —BEL A review shows that most tropical deforestation is associated, directly or indirectly, with agriculture. BACKGROUND Agricultural expansion is a primary cause of tropical deforestation and therefore a key driver of greenhouse gas emissions, biodiversity loss, and the degradation of ecosystem services vital to the livelihoods of forest-dependent and rural people. However, agriculture-driven deforestation can take many forms, from the direct expansion of pastures and cropland into forests to more complex or indirect pathways. A clear understanding of the different ways in which agriculture drives deforestation is essential for designing effective policy responses. To address this need we provide a review of the literature on pantropical agriculture-driven deforestation and synthesize the best available evidence to quantify dominant agricultural land-use changes relating to deforestation. We consider the policy implications of this assessment, especially for burgeoning demand-side and supply-chain interventions seeking to address deforestation. ADVANCES New methods and data have advanced our understanding of deforestation and subsequent land uses. However, only a handful of studies estimate agriculture-driven deforestation across the entirety of the tropics. Although these studies agree that agriculture is the dominant land use following forest clearing, their estimates of pantropical rates of agriculture-driven deforestation during the period 2011 to 2015 vary greatly—between 4.3 and 9.6 million hectares (Mha) per year—with our synthesized estimate being 6.4 to 8.8 Mha per year. This apparent uncertainty in the amount of agriculture-driven deforestation can be disentangled by distinguishing between the different ways in which agriculture contributes to deforestation; we find that while the overwhelming majority (90 to 99%) of tropical deforestation occurs in landscapes where agriculture is the dominant driver of tree cover loss, a smaller share (45 to 65%) of deforestation is due to the expansion of active agricultural production into forests. Multiple lines of evidence show that the remainder of agriculture-driven deforestation does not result in the expansion of productive agricultural land but instead is a result of activities such as speculative clearing, land tenure issues, short-lived and abandoned agriculture, and agriculture-related fires spreading to adjacent forests. Different land uses and commodities often interact to drive deforestation. However, pasture expansion is the most important driver by far, accounting for around half of the deforestation resulting in agricultural production across the tropics. Oil palm and soy cultivation together account for at least a fifth, and six other crops—rubber, cocoa, coffee, rice, maize, and cassava—likely account for most of the remainder, with large regional variations and higher levels of uncertainty. OUTLOOK This Review points to three key areas where a stronger evidence base would advance global efforts to curb agriculture-driven deforestation: First, consistent pantropical data on deforestation trends are lacking. This limits our ability to assess overall progress on reducing deforestation and account for leakage across regions. Second, with the exception of soy and oil palm the attribution of deforestation to forest risk commodities is often based on coarse-grained agricultural statistics, outdated or modeled maps, or local case studies. Third, uncertainties are greatest in dry and seasonal tropics and across the African continent in particular. This assessment highlights that although public and private policies promoting deforestation-free international supply chains have a critical role to play, their ability to reduce deforestation on the ground is fundamentally limited. One-third to one-half of agriculture-driven deforestation does not result in actively managed agricultural land. Moreover, the majority—approximately three-quarters—of the expansion of agriculture into forests is driven by domestic demand in producer countries, especially for beef and cereals, including much of the deforestation across the African continent. These data suggest that the potential for international supply chain measures to help reduce tropical deforestation is more likely to be achieved through interventions in deforestation risk areas that focus on strengthening sustainable rural development and territorial governance. Agriculture contributes to deforestation in many ways which often interact. Most tropical deforestation occurs in landscapes where agriculture is the dominant driver of forest loss. Part of this agriculture-driven deforestation results in agricultural production (left) meeting domestic and export demand for various agricultural commodities. However, agriculture-driven deforestation also occurs without expansion of managed agricultural land through several mechanisms (right), which may lead to the deforested area being abandoned or semi-abandoned. Incomplete agricultural records also explain a share of such deforestation.

V. K. Quy, Nguyen Van Hau, Dang Van Anh et al.

The growth of the global population coupled with a decline in natural resources, farmland, and the increase in unpredictable environmental conditions leads to food security is becoming a major concern for all nations worldwide. These problems are motivators that are driving the agricultural industry to transition to smart agriculture with the application of the Internet of Things (IoT) and big data solutions to improve operational efficiency and productivity. The IoT integrates a series of existing state-of-the-art solutions and technologies, such as wireless sensor networks, cognitive radio ad hoc networks, cloud computing, big data, and end-user applications. This study presents a survey of IoT solutions and demonstrates how IoT can be integrated into the smart agriculture sector. To achieve this objective, we discuss the vision of IoT-enabled smart agriculture ecosystems by evaluating their architecture (IoT devices, communication technologies, big data storage, and processing), their applications, and research timeline. In addition, we discuss trends and opportunities of IoT applications for smart agriculture and also indicate the open issues and challenges of IoT application in smart agriculture. We hope that the findings of this study will constitute important guidelines in research and promotion of IoT solutions aiming to improve the productivity and quality of the agriculture sector as well as facilitating the transition towards a future sustainable environment with an agroecological approach.

P. M. Ngegba, G. Cui, M. Khalid et al.

Pest management is being confronted with immense economic and environmental issues worldwide because of massive utilization and over-reliance on pesticides. The non-target toxicity, residual consequence, and challenging biodegradability of these synthetic pesticides have become a serious concern, which urgently requires the alternative and prompt adoption of sustainable and cost-effective pest control measures. Increasing attention in environmental safety has triggered interest in pest control approaches through eco-friendly plant-based pesticides. Botanical pesticidal constituents are effective against myriads of destructive pests and diseases. More importantly, they are widely available, inexpensive, accessible, rapidly biodegradable, and have little toxicity to beneficiary agents. The phytochemical compositions in diverse plant species are responsible for their varying mechanisms of action against pests and diseases. However, difficulties in their formulation and insufficient appropriate chemical data have led to a low level of acceptance and adoption globally. Therefore, the review seeks to highlight the status, phytochemical compositions, insecticidal mechanisms, and challenges of plant-based pesticide usage in sustainable agricultural production.

Dorijan Radočaj, Ante Šiljeg, Rajko Marinović et al.

Vegetation indices provide information for various precision-agriculture practices, by providing quantitative data about crop growth and health. To provide a concise and up-to-date review of vegetation indices in precision agriculture, this study focused on the major vegetation indices with the criterion of their frequency in scientific papers indexed in the Web of Science Core Collection (WoSCC) since 2000. Based on the scientific papers with the topic of “precision agriculture” combined with “vegetation index”, this study found that the United States and China are global leaders in total precision-agriculture research and the application of vegetation indices, while the analysis adjusted for the country area showed much more homogenous global development of vegetation indices in precision agriculture. Among these studies, vegetation indices based on the multispectral sensor are much more frequently adopted in scientific studies than their low-cost alternatives based on the RGB sensor. The normalized difference vegetation index (NDVI) was determined as the dominant vegetation index, with a total of 2200 studies since the year 2000. With the existence of vegetation indices that improved the shortcomings of NDVI, such as enhanced vegetation index (EVI) and soil-adjusted vegetation index (SAVI), this study recognized their potential for enabling superior results to those of NDVI in future studies.