Abstract

In an era of global environmental challenges, understanding the dynamics of food production is crucial, particularly in regions prone to food insecurity and susceptible to climatic variations. Despite extensive research on agriculture in sub-Saharan Africa (SSA), a thorough examination of the combined effects of various determinants, including food prices and institutional quality, on food security remains limited. Using panel data from 2002 to 2020, this study explores the effects of agricultural land, population growth, environmental degradation, income per capita, food prices, capital formation, and institutional quality on food security in 32 SSA countries. Based on the Pedroni and Kao cointegration test outcomes, a long-run correlation between food security and its influencing factors is evident. The findings from the pooled mean group (PMG) models reveal that extended agricultural land leads to enhanced food security both in the short- and long-run. Likewise, population expansion, rising per capita income, and capital formation drive higher food demand, contributing positively to food security outcomes. Conversely, environmental degradation poses a significant threat, impairing food security in the SSA. Mixed results are observed with food prices, where higher prices can both enhance and reduce food security. The poor institutional quality in SSA correlates with food insecurity. Importantly, the Dumitrescu–Hurlin causality test results reveal bidirectional causality between food security and most variables, except for food inflation and institutional quality. The method of moments quantile regression (MMQR) strengthens the robustness of the study findings. Building on these insights, the study recommends focusing on sustainable land use practices, effective environmental management strategies, increased agricultural investment, governance reforms, and implementing balanced pricing mechanisms.

Introduction

Food security remains a prominent global concern, as asserted in Sustainable Development Goal 2 of the 2030 Agenda. The goal of ending hunger, food insecurity, and undernutrition has been the focus of recent studies [ 1 , 2 ]. Ensuring access to food and its availability is vital for enhancing human development and potential, as it plays a critical role in strengthening human capabilities [ 3 ]. Numerous interrelated variables, including population expansion, climatic issues, conflicts, and land degradation, have threatened global food security [ 4 , 5 ]. Since the beginning of the industrial age, greenhouse gas (GHG) emissions have increased, resulting in greater radiative force that affects the atmosphere, leading to the warming of the earth’s surface and climatic changes [ 6 ]. Consequently, elevated air temperatures, heavy rainfall, and prolonged droughts affected water availability and agricultural yield, ultimately leading to a reduction in food availability and compromising food security [ 7 ]. Environmental shifts greatly affect the lives of rural residents, particularly in sub-Saharan Africa (SSA), who rely predominantly on agriculture for sustenance [ 8 ]. Hence, recognizing and adopting sustainable development strategies is essential to ensuring food security and self-reliance within the agricultural sector while also meeting the growing need for food in the face of land degradation [ 9 ].

Evaluating food security is crucial for aid, famine risk monitoring, nutrition assessment, and policy shaping, requiring global attention from professionals, policymakers, and researchers [ 10 ]. As FAO et al. [ 11 ] highlight, food security is a binary state—a person can be either food-secure or insecure. Food security has continuously acquired prominence and economic relevance since the 1974 World Food Conference, which focused heavily on issues related to hunger, famine, and the food crisis [ 12 , 13 ]. Although the description of food security has developed over time, it continues to denote a condition where all individuals consistently possess physical, social, and economic access to sufficient, nutritionally adequate food that meets their dietary preferences and requirements, enabling them to maintain active and healthy lifestyles. The four main pillars of this comprehensive definition are utilization, affordability, stability, and availability [ 14 , 15 ]. The declining production of major food crops due to environmental degradation has food security implications in low-income countries [ 2 ]. Besides, Pérez-Escamilla [ 16 ] points out that inadequate food has harmful consequences for physical, emotional, and cognitive well-being, disrupting social and environmental balance with far-reaching implications. Additionally, Burchi and De Muro [ 17 ] suggested that inadequate education, health, and essential life skills can contribute to food insecurity.

Conclusion

In the context of mounting global environmental trials, it becomes imperative to comprehend the complexities of food security dynamics, especially in areas grappling with food shortages, vulnerable to climatic fluctuations, and undergoing rapid demographic transformations. Although there is a substantial body of research addressing agriculture in sub-Saharan Africa, a comprehensive analysis of the collective impact of diverse factors, such as food pricing dynamics and the quality of institutional frameworks, on food security is still notably scarce. Therefore, this study utilizes panel data from 2002 to 2020 to examine the effects of agricultural land, population growth, environmental degradation, food prices, gross capital formation, and institutional quality on food security in 32 SSA countries. The study identified cross-sectional dependence to apply panel cointegration methods such as PMG and MG and refuted the null hypothesis that the slope coefficients are homogeneous. Regarding this, we employed second-generation unit root tests, specifically CADF and CIPS, to ascertain the integration order of the variables, revealing a mixed stationarity of I (0) and I (1). Furthermore, the long-run cointegration relationship between the scrutinized variables and food security was established through Pedroni and Kao cointegration tests. The MMQR analysis confirmed the robustness of the long-run findings obtained through the PMG method. Additionally, the study utilized the Dumitrescu-Hurlin test to ascertain the causality pathways among these variables.

Abstract

CONTEXT

     Automated monitoring of the soil-plant-atmospheric continuum at a high spatiotemporal resolution is a key to transform the labor-intensive, experience-based decision making to an automatic, data-driven approach in agricultural production. Growers could make better management decisions by leveraging the real-time field data while researchers could utilize these data to answer key scientific questions. Traditionally, data collection in agricultural fields, which largely relies on human labor, can only generate limited numbers of data points with low resolution and accuracy. During the last two decades, crop monitoring has drastically evolved with the advancement of modern sensing technologies. Most importantly, the introduction of IoT (Internet of Things) into crop, soil, and microclimate sensing has transformed crop monitoring into a quantitative and data-driven work from a qualitative and experience-based task.

OBJECTIVE

     Ag-IoT systems enable a data pipeline for modern agriculture that includes data collection, transmission, storage, visualization, analysis, and decision-making. This review serves as a technical guide for Ag-IoT system design and development for crop, soil, and microclimate monitoring.

METHODS

     It highlighted Ag-IoT platforms presented in 115 academic publications between 2011 and 2021 worldwide. These publications were analyzed based on the types of sensors and actuators used, main control boards, types of farming, crops observed, communication technologies and protocols, power supplies, and energy storage used in Ag-IoT platforms.

RESULTS AND CONCLUSION

     The result showed that 33 variables measured by various sensors were demonstrated in these studies while 10 actuations were successfully integrated with Ag-IoT platforms. Perennial crops, which introduced less disturbance to Ag-IoT platforms than annual crops, were selected by 64% of researchers. Furthermore, studies in Ag-IoT system development were more focused on outdoor than indoor environments. Ag-IoT systems based on Arduino were most common among the studies while commercial platforms were least adopted, likely due to their inflexibility in customized developments. More researchers focused on agricultural applications than the IoT technology itself. Soil water content-based irrigation scheduling and controlled environment monitoring and controlling were the main applications. Other application areas included soil nutrient estimation, crop monitoring based on multiple vegetation indices, pest identification, and chemigation.

SIGNIFICANCE

     Several potential future research directions were identified at the end of the review, including integration of satellite-based internet connectivity to improve the IoT networks in non-connected farms, development of mobile IoT platforms (drones and autonomous ground vehicles) with continuous connectivity, and the use of edge-computing and machine-learning/deep-learning to enhance the capability of the Ag-IoT systems.

Introduction

     Crops are essential for human life because they provide food, animal feed, fuel, and raw materials for clothing and shelter. Crop yield has to be doubled in 2050 compared to 2009 in order to meet the demand of a growing population while increasing the food quality and reducing production inputs (Fukase and Martin, 2020). Potential solutions to enhance global food security include closing crop yield gaps, reducing food waste, changing dietary habits, and reducing inefficiencies in resource use (Foley et al., 2011). Reducing inefficiencies in input resources (such as water and nitrogen) can be achieved by continuously monitoring crops, soil, and microclimate, and then properly controlling inputs without sacrificing the yield and quality of the crop. Internet of Things (IoT) becomes a key technology that enables continuous monitoring and control in this scenario. The ability to generate (near) real-time quantitative data with high spatiotemporal resolution is a major advantage of IoT systems (Liao et al., 2017). IoTs are considered big data systems due to volumes, velocities, and varieties of data they generate. These data are mined and modeled to elucidate the relationships between inputs and outputs (Tsai et al., 2014). Correlation, trend analysis, classification, and numerical prediction are implemented on the data to reach meaningful control decisions. Compared with the conventional wireless sensor networks, the holistic approach of IoT technology allows users to incorporate data analytics on the big data collected by IoT sensor devices. Generally, connected actuators are enabled to control the inputs to achieve desired application rates. For example, an internet-connected soil water content (SWC) sensor network measures the plant water deficit and uploads data to a cloud-based data analysis platform. The analysis will find the trend of soil water deficit to determine the best time and quantity to apply irrigation water.

Conclusions and future directions

Conclusions

     Ag-IoT is a promising technology that would increase resource use efficiency in agricultural systems, and is an essential tool for digital agriculture transformation. In this paper, we have overviewed impactful research related to Ag-IoT in the past decade. The data collected from these papers were categorized and analyzed under six main Ag-IoT system design parameters namely sensors, sensing platforms and main control board, communication technology and IoT protocols, cloud platforms, power and energy management, and actuators. According to the analyzed data, it is revealed that there is an increased global attention towards the Ag-IoT system-related research in the recent years. However, there are certain research gaps found in the literature. One of them is that the implementations of the sensors and the actuators seem to be limited to soil and environmental parameter monitoring and irrigation controlling. Furthermore, crop macro and micronutrient demand analyses are still at the infant stage due to the non-availability of sensors that can measure nutrients in real-time. Therefore, it is essential to improve the sensor and actuator applications in crop monitoring and controlling. In addition, heterogeneity of the system parameters (such as data, platforms, required power) is a major challenge to the Ag-IoT systems implementation, to which the improvement of the context-awareness could be a solution. Power harness options for Ag-IoT nodes need more exploration as there are limited options available and it would be a big advantage for the perennial crop monitoring. The perception and the network layers of Ag-IoT systems require more improvements to meet the sensor implementation ergonomics and long-range high-throughput data transmission, respectively. Edge computing can be a replacement of the high throughput long-range communication, but to the best of our knowledge, only a limited number of practical applications have been developed based on edge computing to date. Mobile Ag-IoT platforms such as unmanned aerial and ground vehicles have a huge potential to increase the spatiotemporal resolution in Ag-IoT-based monitoring and controlling.

Abstract

     Recent advances in communication technologies with the emergence of connected objects have changed the agricultural area. In this new digital age, the development of artificial intelligence, particularly deep learning, has allowed for acceleration and improvement in the processing of collected data. To highlight the evolution and advances observed in deep learning in agriculture, we conducted a bibliometric study on more than 400 recent research studies. The analyses carried out on recent research works suggest that deep learning is widely involved in the digitization of agriculture areas with high accuracy exceeding the standard image processing techniques. Most of the works focus on crop classification problems, weed, and pest identification. Their methods are mainly based on convolutional neural network architecture. From the cases study, we have identified three key challenges that are essential in the deep learning methods applied in agriculture: (i) the need to consider the perception of the domain actors, their appropriation or interaction with the existing tools; (ii) the requirement to perform statistical tests to analyze the performance of the classifiers resulting from the learning process; and (iii) the need to perform statistical cross-validations with the training data. In the end, we summarized the agricultural data processing process consisting of several parts, for a better consideration of the expectations resulting from the challenges addressed. We consider that this study can serve as a guideline of research for the scientist and practician in the application of deep learning methodology in agriculture.

Introduction

     Agriculture is the practice of growing food. Given the increase in population, this sector must meet many requests for food while considering societal (such as labor), environmental (water scarcity, loss of biodiversity, land degradation, etc.), and economic issues. The constraints of its development have become numerous given the seasonal variability and extreme climate. It is more necessary than ever to find innovative practices for the development of the agricultural sector.

     Digital integration has significantly changed farmers’ knowledge of field management with innovative technologies like intelligent computing, robotics, drones, or sensors onboard farm machinery. Using these technologies is encouraging data scientists and agronomists to design analytical tools and techniques to accurately organize field management and address the new challenges at hand (fungal attack detection, crop yield prediction, advanced spraying, etc.). These novel practices require farmers’ technical assistance to support their needs and help them maximize their crop yields based on data and task automation.

Conclusion

     Precision agriculture is a recent science that is certainly still in its young phase. Deep learning can make a decisive contribution to the analysis of agricultural data, in this case by using computer vision to enable a machine to automatically analyze and understand the visual world. This offers the opportunity to develop intelligent systems, which appear to be one of the many possible ways to tackle the economic, environmental, and social challenges in agriculture. This literature review shows a richness of work in recent years that describe the development of various systems using deep learning to solve various problems in precision agriculture. The problems addressed are mainly yielded losses caused by factors affecting crop growth. In bibliometric methodology, we explored only documents from the Web of Science database. We analyzed the thematic structures and new directions of agricultural research based on deep neural networks were proposed.

     Bibliometric analyses show that the most active countries are China, the United States, India, and Brazil. The hot topics covered include plant disease, feature extraction, transfer learning, disease detection, weed detection, insect pests, convolutional neural networks, image processing, remote sensing, and IoT. Convolutional neural networks (CNNs) are one of the most used architectures thanks to their successful application in deep learning. However, the use of deep learning algorithms in precision agriculture raises many challenges whose resolution is useful to enhance this field and facilitate its appropriation by the stakeholders:

Abstract

1. Introduction

2. Fundamentals of nanotechnology

3. The role of nanotechnology in achieving food security

4. Nanotubes in food and agriculture

5. Nanosensors in food and agriculture

6. The challenges of nanotechnology in food and agriculture system

7. Conclusions

References

Abstract

     Ensuring food security in developing countries is highly challenging due to low productivity of the agriculture sector, degradation of natural resources, high post farming losses, less or no value addition, and high population growth. Researchers are striving to adopt newer technologies to enhance supply to narrow the food demand gap. Nanotechnology is one of the promising technologies that could improve agricultural productivity via nano fertilizers, use of efficient herbicides and pesticides, soil feature regulation, wastewater management, and pathogen detection. It is equally beneficial for industrial food processing with enhanced food production with excellent market value, elevated nutritional and sensing property, improved safety, and better antimicrobial protection. Nanotechnology can also reduce post-farming losses by increasing the shelf life with the aid of nanoparticles. However, further investigation is required to solve the safety and health risks associated with the technology.

Introduction

     The foremost universal challenge on our planet is the question of establishing food security for a rapidly increasing population in the world. Predictions show that food demand is likely to rise from 59 to 98% for the world population reaching 9 billion by 2050 (Duro et al., 2020). Despite an increase of the world population particularly in developing countries, the global food supply interrupted by the expenditure of bio-resources for production of energy, manufacturing chemicals, high post farming loss, less value addition, inefficient distribution and marketing systems, and other factors (Barrett, 2021; Sekhon, 2014). Farmers throughout the world will focus on using new innovations and technologies for enhancing the production of crops through intensive and extensive agriculture. The current efforts further boosted through the use of nano-modified stimulants and precision farming. Agricultural efficiency, soil improvement, secure water use, distribution of food in stores, and its quality are basic factors of securing food that may be improved via advances in nanotechnology research (Ashraf et al., 2021; Sastry et al., 2011).

     Newer technology that will increase the production and reduce food wastage is important to maintain sustainable living standards of the nation and improve food security. Nanotechnology can provide a path for producing foods with outstanding quality in highly improved workable form along with induction of nutrients bioavailability. Many research investigations are focusing on increasing the application of nanotechnology for the production of crop and food processing (Abobatta, 2018; Axelos and Van De Voorde, 2017; Dasgupta et al., 2015; Peters et al., 2016). Increment in articles, intellectual property, and patents in nano-agriculture-based food with fresh research tendencies in the processing of food, nutraceutical distribution, packing, quality control, and serviceable food is a highly expanding field in nanotechnology research (Dasgupta et al., 2015).

Conclusions

     Nanotechnology is a newly emerging, but highly expanding technology in many fields related to human activities and benefits worldwide. Its fascinating phenomena have been witnessed through several research findings that the nanoparticles and nanostructure improve various properties due to small size, larger surface area, and highly catalytic nature. Nanotechnology is crucial in achieving food security, especially in the agriculture sector. It can improve crop production by effective microbial, pest, and weed control with high economic value, security, and safety. It also plays a key role in food processing, food modification, stability, sensing, prolonged shelf life, minimized food losses, and provide safe food. Nanotechnology also minimizes post-harvest losses with better stability, safety, and packaging materials. Nanoparticles like Ag, Au, Zn, TiO2, ZnO, SiO2, and MgO commonly used in food processing, may also induce health risks due to easy penetration through the cell for adverse reactions within various humans, and animal organs as well as parts of plants. Such risks due to nanoparticles or nanocomposite could be controlled in future research via using greener synthesis and searching for easy and cheaper protocols for degradation and removal of existing nanomaterial from the sites of attack.

Abstract

Keywords

1. Introduction

2. IoT in smart farming

3. Challenges in the current generation of mobile networks

4. Rise of 5G

5. Application of 5G in agriculture

6. Future outlook

7. Conclusion

Declaration of Competing Interest

Acknowledgments

References

Abstract

Over the next decade, the superfast 5G network will play a critical role in farming industries to improve the yields and quality of crops while using minimal labor. Smart and precision farming allows farmers to be more informed and productive. The advent of 5G will considerably change the nature of jobs in farming and agriculture. The internet of things (IoT)-based cloud computing service in the 5G network provides flexible and efficient solutions for smart farming. This will allow the automated operation of various unmanned agricultural machines for the plowing, planting, and management phases of crop farming and will ultimately achieve secure, reliable, environmentally friendly, and energy-efficient operations and enable unmanned farms. This paper provides a complete survey on 5G technology in the agricultural sector and discusses the need for and role of smart and precision farming; benefits of 5G; applications of 5G in precision farming such as real-time monitoring, virtual consultation and predictive maintenance, data analytics and cloud repositories; and future prospects.

1. Introduction

Agriculture is the primary source of livelihood and plays a vital role in most countries’ economies. Agriculture is not only associated with crop production but also includes animal breeding and land cultivation to offer food, fiber, and medicine. Different types of agriculture are practiced in different regions across the world, focusing primarily on providing healthy food to feed the population worldwide. Agriculture is the main source of income for developing countries. It ensures food security for a country and produces material for industry. Modern farming started approximately in the 18th century, referred to as the British Agricultural Revolution, when in a short period, many improvements were made to farming, leading to a major yield increase and more efficient methods. A four-field system and selective cross-breeding program were implemented to increase crop size as well as yields, replacing the three-field crop rotation system. The first subsequent revolution in agriculture took place between 1900 and the 1930s, when mechanized agriculture allowed each farmer to produce enough for 26 people. This agricultural revolution brought techniques such as soil management and the advent of various new farming tools. After many decades, a second revolution, known as the Green Revolution, took place in the 1990s. Genetically modified crops that are pest resistant and need less water were introduced with the help of scientific advancements, allowing each farmer to feed 155 people. The second revolution promoted the use of mechanized tools in farming practices, increasing the rate of production as well as the quantity of crop yield. The third revolution, also described as a green revolution, was the phase when genetically modified crops began to be used by almost everyone, leading to greater produce output.

Abstract

Keywords

1. Introduction

2. Overview of advanced image recognition technologies

3. Advanced image recognition technologies of agricultural diseases

3.1. Methods based on deep learning

3.1.1. Deep learning models

3.1.2. Works based on deep learning

3.1.3. Discussion

3.2. Methods based on transfer learning

3.2.1. Transfer learning models

3.2.2. Works based on transfer learning

3.2.3. Discussion

3.3. Summary

4. Conclusion

Declaration of Competing Interest

Acknowledgment

References

Abstract

Agricultural disease image recognition has an important role to play in the field of intelligent agriculture. Some advanced machine learning methods associated with the development of artificial intelligence technology in recent years, such as deep learning and transfer learning, have begun to be used for the recognition of agricultural diseases. However, the adoption of these methods continues to face a number of important challenges. This paper looks specifically at deep learning and transfer learning and discusses the recent progress in the use of these advanced technologies for agricultural disease image recognition. Analysis and comparison of these two methods reveals that current agricultural disease data resources make transfer learning the better option. The paper then examines the core issues that require further study for research in this domain to continue to progress, such as the construction of image datasets, the selection of big data auxiliary domains and the optimization of the transfer learning method. Creating image datasets obtained under actual cultivation conditions is found to be especially important for the development of practically viable agricultural disease image recognition systems.

1. Introduction

A recent report by the Food and Agriculture Organization of the United Nations suggests that more than one third of the natural loss of agricultural production every year is caused by agricultural diseases and pests [1], making these the most important factors currently affecting agricultural production and food security [2]. Agricultural production is complex and there are numerous agricultural diseases and pests that need to be taken into account. Traditional approaches that rely on laboratory-based observations and experiments can easily lead to incorrect diagnoses.

1. Introduction

2. Literature survey

3. Artificial neural networks in agriculture

4. Automation and wireless system networks in agriculture

5. Implementation of fuzzy logic systems in agriculture

6. Proposed idea

7. Future scope

8. Conclusion

Acknowledgements

References

Abstract

Agriculture automation is the main concern and emerging subject for every country. The world population is increasing at a very fast rate and with increase in population the need for food increases briskly. Traditional methods used by farmers aren’t sufficient enough to serve the increasing demand and so they have to hamper the soil by using harmful pesticides in an intensified manner. This affects the agricultural practice a lot and in the end the land remains barren with no fertility. This paper talks about different automation practices like IOT, Wireless Communications, Machine learning and Artificial Intelligence, Deep learning. There are some areas which are causing the problems to agriculture field like crop diseases, lack of storage management, pesticide control, weed management, lack of irrigation and water management and all this problems can be solved by above mentioned different techniques. Today, there is an urgent need to decipher the issues like use of harmful pesticides, controlled irrigation, control on pollution and effects of environment in agricultural practice. Automation of farming practices has proved to increase the gain from the soil and also has strengthened the soil fertility. This paper surveys the work of many researchers to get a brief overview about the current implementation of automation in agriculture. The paper also discusses a proposed system which can be implemented in botanical farm for flower and leaf identification and watering using IOT.

Introduction

With the advent of technology in this digital world, we humans have pushed our limit of the thinking process and are trying to coalesce normal brain with an artificial one. This continuing exploration gave birth to a whole new field Artificial intelligence. It is the process by which a human can make an intelligent machine. AI comes under the domain area of computer science which can be able to discern its milieu and should thrive to maximize the rate of success. AI should be able to do work based on past learning. Deep learning, CNN, ANN, Machine learning are certain domains which enhances the machine work and helps to develop a more advance technology

The term IOT is elucidated as “thing to thing” communication. The three main targets are communication, automation and cost saving in the system. Dr. D.K. Sreekantha, Kavya.A.M provides the in-depth application of IOT in the field of agriculture and how it can be helpful to the humans.

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.

Introduction

Human beings have cultivated lands and breed animals to obtain food for their survival since ancient times. This practice, known as agriculture, has evolved following a long-term and progressive process (Tekinerdogan, 2018), going from Agriculture 1.0 to 4.0, as shown in Fig. 1. In Fig. 1, Agriculture 1.0 refers to the traditional agricultural era, mainly replying on the manpower and animal forces. In this stage, though simple tools like sickles and shovels were used in agricultural activities, humans still cannot get rid of heavy manual labor, so productivity remained at a low level. Until the 19th century, steam engines were improved and widely used to provide new powers in all walks of life and industries, including agriculture. It came to the era of Agriculture 2.0 when various agricultural machineries were operated by farmers manually and plenty of chemicals were used. Obviously, Agriculture 2.0 significantly increased the efficiency and productivity of farm works. Nevertheless, this substantial improvement brought too harmful consequences: field chemical contaminations, destruction of the ecological environment, excessive consumption of powers, and waste of natural resources.

Occurrence and Functions of Mucilage

A Model of a Mucilage-Supported Diazotrophic Microbiota

Disassembly of the Complex Mucilage Polysaccharide

Sugar Utilization to Support Nitrogenase Activity

Reduction of Oxygen Levels in the Mucilage Environment

Maintaining Low Nitrogen Levels in the Mucilage Environment

Concluding Remarks

Acknowledgement

References

Role for Nitrogen Fixation in Cereal Crops

Nitrogen-fixing microbial associations with nonlegumes, especially cereals, have been a topic of intense interest for more than a century, since such associations could reduce the requirement for nitrogen fertilizers [1–4]. For instance, sugarcane associates with diazotrophic endophytes that contribute to its nitrogen nutrition in some environments [5–7] and a study based on 15N dilution experiments in Miscanthus × giganteus concluded that this bioenergy feedstock could acquire about 16% of its nitrogen from the air [8]. These examples indicate that some monocots have the potential to associate with diazotrophs and acquire small, but significant, amounts of fixed nitrogen from the atmosphere. It has been shown that the model C4 grass Setaria viridis can obtain most of its fixed N from associative nitrogen fixation following inoculation with diazotrophs [9]. The possibility that cereal crops obtain a significant proportion of total N by associative nitrogen fixation has also been suggested by a 50-year assessment of global nitrogen budgets in maize (Zea mays), rice (Oryza sastiva), and wheat (Triticum aestivum), which concluded that up to 24% of the total nitrogen in these crops was derived from nonlegume symbiotic nitrogen fixation [10]. Interestingly, mucilage on aerial roots of sorghum (Sorghum bicolor) with an inset micrograph of Azospirillum brasilense was highlighted on the cover page of the proceedings of a conference on cereal nitrogen fixation held in India in 1984 [11], suggesting that it was suspected more than 35 years ago that sorghum mucilage harbors a diazotrophic microbiota. However, no report on nitrogen fixation in sorghum mucilage was published in the 1984 proceedings volume or elsewhere.

Occurrence and Functions of Mucilage

A key feature of the Sierra Mixe maize landrace mucilage is the abundance of sugars that potentially serve as a source of energy for the diazotrophs. The monosaccharide composition indicated that the mucilage primarily comprises fucose (41%), galactose (36%), arabinose (14%), xylose (3%), glucuronic acid (3%), and mannose (3%) [12]. This composition is similar to the mucilage reported for maize underground roots [15,16]. Osborne et al. found that underground root mucilage contained fucose (61.0%) and glucose (31.4%), while Chaboud found the mucilage to contain galactose (30.7%), fucose (19.3%), glucose (18.5%), xylose (15.2%), and arabinose (13.4%). Both of these previous studies showed high amounts of fucose, xylose, arabinose, and galactose, which agrees qualitatively with the analysis of the Sierra Mixe aerial root mucilage. This monosaccharide composition is not commonly found in plant cell wall polysaccharides and may play a role in signaling associative diazotrophic bacteria that can degrade the mucilage complex polysaccharide and use the released monosaccharides to support growth and nitrogen fixation.

1- Introduction

2- Sources and characterization of agricultural wastewater

3- Struvite and its recovery performance from agricultural wastewaters

4- Commercial development of struvite products

5- Hindrances to struvite recovery from agricultural wastes

6- Strategies for performance intensification and operational cost reductions

7- Use of struvite as an alternative fertilizer in the soil

8- Economic evaluation

9- Conclusions

References

Abstract

To meet the needs of a fast growing global population, agriculture and livestock production have been intensified, resulting in environmental pollution, climate change, and soil health declining. Closing the nutrient circular loop is one of the most important sustainability factors that affect these issues. Apart from being a serious environmental issue, the discharge of N and P via agricultural wastewater is also a major factor that disturbs nutrient cycling in agriculture. In this study, the performance, in terms of recovery, of N and P (individually, as well as simultaneously) from agricultural wastewaters via struvite has been comparatively summarized. Details on the hindrances to nutrient recovery through struvite formation from agricultural effluents, along with strategies to overcome these hindrances, are provided. In addition, various strategies for recovery performance intensification and operational cost reduction are comprehensively discussed. This work will provide scientists and engineers with a better idea on how to solve the bottlenecks of this technique and integrate it successfully into their treatment systems, which will ultimately help close the nutrient loop in agriculture.

Introduction

The manifestations of rapid population growth, urbanization, improved standards of living, and concurrent intensification of socioeconomic activities on overall environmental health are well recognized and acknowledged (Cordell et al., 2009; Clarke, 2013). Global cereal production has doubled in the past 40 years, mainly from the increased yields resulting from greater inputs of fertilizer, water, pesticides, and so on. This has increased the global per capita food supply and alleviate hunger in poverty-stricken areas (Alexandratos and Bruinsma, 2012). During this process, however, the increase in nitrogenous fertilizer application and exhaustion of the limited reserves of rock phosphate have been quite considerable. At present, the annual fertilizer consumption of rock phosphate is reported to be over one million tons, while the use of N fertilizer could be three times as much (Rahman et al., 2011). Moreover, it is estimated that within 100 years, mined P rocks will be completely exhausted (Cordell and White, 2014”). Global cycling of these nutrients has been altered, owing to their widespread use in intensive agriculture, in ways that can contribute to severe environmental issues. Predominantly, nutrients can escape from farm fields to the surrounding soils, air, and waterways, when applied in excess of the plants’ needs (Deng et al., 2006). Hence, the notion of a closed-loop nutrient cycle provides a simple, persuasive, and elegant approach for realizing efficient natural resource management-improved human well-being, and long-term food security (Maurer et al., 2006). The closing of the nutrient loop includes a wide range of ongoing efforts to make sure that nutrients are applied at times and places that align with the requirements of the plants. It also includes efforts to recover nutrients in usable forms from waste effluents and recycle them into cropping systems (Yorgey, 2014). The logic is that by recovering nutrients from waste effluents, a more “closed” system for sustainable agricultural development can be created.

Key Features of Pollen Germination in Cereals

Genetic and Proteomic Screening for Key Factors in Pollen Germination

Key Pathways Identified for Pollen Germination in Cereals

Concluding Remarks

References

Germination of Pollen

In flowering plants, the distribution of pollen (see Glossary) across land by wind and/or animals drives the spread of genetic variation within a species. Agriculturally, successful fertilization of flowers in an increasingly harsh and unpredictable climate is the key determinant of grain production and yield in cereal crops [1]. Mature pollen usually contains only two cell types – sperm cells and the much larger vegetative (or tube) cell – and desiccates before release from the anther. On adhering to a compatible stigma, pollen grains respond to signals from the mother plant and become active, undergoing cytoplasmic reorganization and activation of stored RNA and protein to produce a pollen tube originating from the vegetative cell. The pollen tube elongates within the pistil until it reaches the egg cell, where the sperm cell divides to enable the double-fertilization event typical of plants (Figure 1, Key Figure).
The factors that control successful pollen germination have been the focus of plant reproduction, evolution, and breeding research to increase crop yield and to overcome hybridization barriers [1,2]. Barriers to fertilization include interspecific incompatibility as well as self-incompatibility, which maintains genetic diversity within a local population. However, overcoming some of these constraints in a breeding environment can contribute to the production of novel elite lines for specific end-uses. Since 2003, pollen transcriptomic studies, together with identified mutants, have rapidly expanded our knowledge of the regulatory mechanisms that govern the fertilization process, including signaling pathways and cytoskeleton proteins [3–11]. However, most of this research has occurred in model plant species such as arabidopsis; despite massive agronomic and economic interest, relatively little is known about these molecular processes in cereal crops (the Poaceae). This review focuses on the unique characteristics of pollen grain adhesion, hydration, germination, and pollen tube growth in monocot cereals, and provides an overview of our current understanding of the regulators governed by the male gametophyte in the best-studied species, rice (Oryza sativa) and maize (Zea mays) (Figure 1C).

Abstract

The root growth of most crop plants is inhibited by soil salinity. Roots respond by modulating metabolism, gene expression and protein activity, which results in changes in cell wall composition, transport processes, cell size and shape, and root architecture. Here, we focus on the effects of salt stress on cell wall modifying enzymes, cellulose microfibril orientation and non-cellulosic polysaccharide deposition in root elongation zones, as important determinants of inhibition of root elongation, and highlight cell wall changes linked to tolerance to salt stressed and water limited roots. Salt stress induces changes in the wall composition of specific root cell types, including the increased deposition of lignin and suberin in endodermal and exodermal cells. These changes can benefit the plant by preventing water loss and altering ion transport pathways. We suggest that binding of Na+ ions to cell wall components might influence the passage of Na+ and that Na+ can influence the binding of other ions and hinder the function of pectin during cell growth. Naturally occurring differences in cell wall structure may provide new resources for breeding crops that are more salt tolerant.

Introduction

Plant evolution has resulted in a large array of mechanisms to tolerate the stresses associated with increased soil salinity. However, for most cereal crops the growth of roots is disrupted when soil salinity exceeds 4 dS/m, equivalent to about 40 mM NaCl. Increased soil salinity exposes plants to ionic sodium (Na+) and chloride (Cl−), which leads to a cascade of responses in the plant due to the ionic and osmotic components of salt stress [1,2].

Salt stress can indirectly affect cell wall properties by causing changes in gene expression, but Na+ can also physically interact with the cell wall components directly, and change their chemical properties [3]. An increase in soil salinity results in accumulation of Na+ in the apoplast, which can lead to an increase in interactions between Na+ and negatively charged sites within cell wall polymers, and also influence apoplastic pH. Salinity causes transient alkalinisation of the apoplast, and this could limit growth in the context of the acid growth theory [4,5]: Auxin activates plasma membrane H+-ATPases and protons are extruded into the apoplast, apoplastic acidification induces cell wall loosening by activating expansins and other remodelling enzymes resulting in loosening of the cell wall. Hence, growth could be limited by a decrease in free apoplastic protons causing a shift in the apoplastic pH away from the range that favors cell-wall loosening [4], although in maize the inhibition of growth as a result of salinity was not associated with the capacity of the epidermal cells to acidify their walls [6].

The land issue is strategically important for any country, and countries like Georgia it is vitally important. In the work below we concentrate attention around the issue of putting agricultural land in the international free market. The problem is analyzed in condition of land price, land rent, demographic situation, agricultural subsidies and so on. As conclusion we represent opinion that, it is unacceptable to put Georgian agricultural land in international free turnover, because country can lose significant part of agricultural land and the possibility of development profitable agricultural sector.

Introduction

There has long been a discussion on land legislation in Georgia. The idea of placing agricultural land on the free international market is still valid, which is allegedly justified by the economic arguments. Proponents of this approach state that the sale of agricultural land to foreign nationals will increase its value and consequently, will increase the capitalization of agricultural enterprises that in turn, will make it easier for farmers to search for resources and will contribute to the growth of economic activity. In classical economic sense, the higher the demand for goods, the higher is the price, although this seemingly doubtless truth, only partially and within certain limits reflects the mechanism of pricing on agriculture land and the justification of rise in prices of Georgian agricultural lands only by this argument is just economic primitivism. In the article, the authors indicate the absurdity of the abovementioned position and, on the basis of a clear economic logic, assert that the placement of agricultural land on the free international market will not promote the development of agriculture in Georgia. In agricultural land pricing, a number of factors should be taken into consideration, of which the most important is the land rent and legislative regulations related to agricultural lands. It is not by accident that the land rent is an important component of the land price determining formula. On the other hand, the essential part of the rent of agricultural land is land capacity. The universally recognized formula for determining land price is as follows: Land price = Rent ir X100% , (1) where rent is an annual rental value of land, but ir is a real average bank interest rate. Rental value of agricultural land is directly linked to land capacity, its quality, proximity to markets and means ♯а communication. As David Ricardo once noted, the land price is determined by the value of grown products, and not the other way around [1]. This finding of the great classicist of economics will be used during our subsequent discussions. At the beginning of article, the authors criticize the allegation that the growing demand for agriculture land is permanently causing an increase in land price. They believe that there is a certain ceiling in this case, after reaching of which, the land actually loses the agricultural status. In order to prove that, first of all, they use precisely the analysis of the land price formula. Rent = Land priceX ir 100% (2) From formula (2), which is a transformed version of formula (1), it follows that in case of a permanent rise in land price, the rent rate should also be increased. However, the basis for agricultural land rent is land capacity, and the value of products grown on this land determines land price, and not vice versa, when the value of agricultural products is determined by land price. Land capacity, as well as the yield, has an upper limit, and these qualities are restricted by the rates of technological development. For more detailed and specific judgment, let’s consider the following example: let’s just say that the land price has increased significantly due to increased demand.