Tailings ponds, large man-made structures conceived during the mining process for waste storage, often become deserted post-mining, leaving behind a stark, contaminated landscape. This paper posits that these forsaken tailings ponds can be rejuvenated into fertile agricultural land through adept reclamation efforts. Serving as a discussion paper, it engages in a stimulating exploration of the environmental and health risks linked to tailings ponds. It sheds light on the potential and impediments in the transformation of these ponds into agricultural land. The discussion concludes that despite the substantial hurdles in repurposing tailings ponds for agriculture, there are encouraging prospects with the application of multifaceted efforts.
Stevia rebaudiana Bertoni is a member of Compositae family. Stevia plant has zero calorie content and its leaves are estimated to be 300 times sweeter than sugar. This plant is believed to be the most ideal substitute for sugar and important to assist in medicinal value especially for diabetic patients. In this study, microcutting techniques using a mist-chamber propagation box were used as it was beneficial for propagation of Stevia and gave genetic uniformity to the plant. The effects of different treatments on root stimulation of Stevia in microcuttings technique were evaluated. Treatments studied were different sizes of shoot cuttings, plant growth regulators, lights, and shades. Data logger was used to record the mean value of humidity (>90% RH), light intensity (673-2045 lx), and temperature (28.6-30.1°C) inside the mist-chamber propagation box. From analysis of variance, there were significant differences between varieties and treatments in parameters studied (P < 0.05). For the size of shoot cuttings treatment, 6 nodes cuttings were observed to increase root number. As compared to control, shoot cuttings treated with indole butyric acid (IBA) had better performance regarding root length. Yellow light and 50% shade treatments showed higher root and leaf number and these conditions can be considered as crucial for potential propagation of Stevia.
Meta-analysis is a subset of systematic review; a technique for systematically combining pertinent qualitative
and quantitative study data from numerous selected studies to broaden a single conclusion that has more
statistical power. This inference is statistically stronger than the analysis of any single study, due to increase
numbers of topics, greater variety amongst subjects, or collected effects and outcomes. The aim of this review
article is to highlight the definition, history, purpose, characteristics, use, advantage, disadvantage, validity,
and steps in conducting meta-analysis.
Insecticides are enormously important to industry requirements and market demands in agriculture. Despite their usefulness, these insecticides can pose a dangerous risk to the safety of food, environment and all living things through various mechanisms of action. Concern about the environmental impact of repeated use of insecticides has prompted many researchers to develop rapid, economical, uncomplicated and user-friendly analytical method for the detection of insecticides. In this regards, optical sensors are considered as favorable methods for insecticides analysis because of their special features including rapid detection time, low cost, easy to use and high selectivity and sensitivity. In this review, current progresses of incorporation between recognition elements and optical sensors for insecticide detection are discussed and evaluated well, by categorizing it based on insecticide chemical classes, including the range of detection and limit of detection. Additionally, this review aims to provide powerful insights to researchers for the future development of optical sensors in the detection of insecticides.
The genus Phytophthora contains more than 100 plant pathogenic species that parasitize a wide range of plants, including economically important fruits, vegetables, cereals, and forest trees, causing significant losses. Global agriculture is seriously threatened by fungicide resistance in Phytophthora species, which makes it imperative to fully comprehend the mechanisms, frequency, and non-chemical management techniques related to resistance mutations. The mechanisms behind fungicide resistance, such as target-site mutations, efflux pump overexpression, overexpression of target genes and metabolic detoxification routes for fungicides routinely used against Phytophthora species, are thoroughly examined in this review. Additionally, it assesses the frequency of resistance mutations in various Phytophthora species and geographical areas, emphasizing the rise of strains that are resistant to multiple drugs. The effectiveness of non-chemical management techniques, including biological control, host resistance, integrated pest management plans, and cultural practices, in reducing fungicide resistance is also thoroughly evaluated. The study provides important insights for future research and the development of sustainable disease management strategies to counter fungicide resistance in Phytophthora species by synthesizing current information and identifying knowledge gaps.
Yearly population growth will lead to a significant increase in agricultural production in the coming years. Twenty-first century agricultural producers will be facing the challenge of achieving food security and efficiency. This must be achieved while ensuring sustainable agricultural systems and overcoming the problems posed by climate change, depletion of water resources, and the potential for increased erosion and loss of productivity due to extreme weather conditions. Those environmental consequences will directly affect the price setting process. In view of the price oscillations and the lack of transparent information for buyers, a multi-agent system (MAS) is presented in this article. It supports the making of decisions in the purchase of sustainable agricultural products. The proposed MAS consists of a system that supports decision-making when choosing a supplier on the basis of certain preference-based parameters aimed at measuring the sustainability of a supplier and a deep Q-learning agent for agricultural future market price forecast. Therefore, different agri-environmental indicators (AEIs) have been considered, as well as the use of edge computing technologies to reduce costs of data transfer to the cloud. The presented MAS combines price setting optimizations and user preferences in regards to accessing, filtering, and integrating information. The agents filter and fuse information relevant to a user according to supplier attributes and a dynamic environment. The results presented in this paper allow a user to choose the supplier that best suits their preferences as well as to gain insight on agricultural future markets price oscillations through a deep Q-learning agent.
The prevalence of organic solid waste worldwide has turned into a problem that requires comprehensive treatment on all fronts. The amount of agricultural waste generated by agro-based industries has more than triplet. It not only pollutes the environment but also wastes a lot of beneficial biomass resources. These wastes may be utilized as a different option/source for the manufacturing of many goods, including biogas, biofertilizers, biofuel, mushrooms and tempeh as the primary ingredients in numerous industries. Utilizing agro-industrial wastes as good raw materials may provide cost reduction and lower environmental pollution levels. Agro-industrial wastes are converted into biofuels, enzymes, vitamin supplements, antioxidants, livestock feed, antibiotics, biofertilizers and other compounds via solid-state fermentation (SSF). By definition, SSF is a method used when there is little to no free water available. As a result, it permits the use of solid materials as biotransformation substrates. Through SSF methods, a variety of microorganisms are employed to produce these worthwhile things. SSFs are therefore reviewed and discussed along with their impact on the production of value-added items. This review will provide thorough essential details information on recycling and the use of agricultural waste.
The use of agro-biowaste compost fertilizers in agriculture is beneficial from technical, financial, and environmental perspectives. Nevertheless, the physical, mechanical, and agronomical attributes of agro-biowaste compost fertilizers should be engineered to reduce their storage, handling, and utilization costs and environmental impacts. Pelletizing and drying are promising techniques to achieve these goals. In the present work, the effects of process parameters, including compost particle size/moisture content, pelletizing compression ratio, and drying air temperature/velocity, were investigated on the density, specific crushing energy, and moisture diffusion of agro-biowaste compost pellet. The Taguchi technique was applied to understand the effects of independent parameters on the output responses, while the optimal pellet properties were found using the iterative thresholding method. The soil and plant (sweet basil) response to the optimal biocompost pellet was experimentally evaluated. The farm application of the optimal pellet was also compared with the untreated agro-biowaste compost using the life cycle assessment approach to investigate the potential environmental impact mitigation of the pelletizing and drying processes. Generally, the compost moisture content was the most influential factor on the density and specific crushing energy of the dried pellet, while the moisture diffusion of the wet pellet during the drying process was significantly influenced by the pelletizing compression ratio. The density, specific crushing energy, and moisture diffusion of agro-biowaste compost pellet at the optimal conditions were 1242.49 kg/m3, 0.5054 MJ/t, and 8.2 × 10-8 m2/s, respectively. The optimal biocompost pellet could release 80% of its nitrogen content evenly over 98 days, while this value was 28 days for the chemical urea fertilizer. Besides, the optimal pellet could significantly improve the agronomical attributes of the sweet basil plant compared with the untreated biocompost. The applied strategy could collectively mitigate the weighted environmental impact of farm application of the agro-biowaste compost by more than 63%. This reduction could be attributed to the fact that the pelletizing-drying processes could avoid methane emissions from the untreated agro-biowaste compost during the farm application. Overall, pelletizing-drying of the agro-biowaste compost could be regarded as a promising strategy to improve the environmental and agronomical performance of farm application of organic biofertilizers.
This study investigated acclimation ability of native Chlorella sorokiniana (CS-N) and commercial Chlorella sorokiniana (CS-C) in palm oil mill effluent (POME), their metabolic profile and feasibility of effluent recycling for dilution purpose. Maximum specific growth rate, µmax and lag time, λ of the microalgae were evaluated. Result shows both strains produced comparable growth in POME, with µmax of 0.31 day-1 and 0.30 day-1 respectively, albeit longer λ by the CS-C. However, three cycles of acclimation was able to reduce λ from eight days to two days for CS-C. Metabolic profiling using principal component analysis (PCA) shows clear cluster of acclimatized strains to suggest better stress tolerance of CS-N. Finally, a remarkable µmax of 0.57 day-1 without lag phase was achieved using acclimatized CS-N in 40% POME concentration. Acclimation has successfully shortened the λ and dilution with final effluent was proved to be feasible for further improvement of the microalgae growth.
In this study, three different methods for high quality solid fuel production were tested and compared experimentally. Oil palm empty fruit bunches, mesocarp fibers, palm kernel shells and rubber seeds shells were treated using thermal (TC), hydrothermal (HTC) and vapothermal (VTC) carbonization. All thermochemical methods were accomplished by using a custom made batch-type reactor. Utilization of novel single reactor equipped with suspended internal container provided efficient operation since both steam generator and raw materials were placed inside the same reactor. Highest energy densification was achieved by VTC process followed by TC and HTC processes. The heating value enhancement in VTC and TC was achieved by the increase in fixed carbon content and reduction in volatile matter. The formation of the spherical components in HTC hydrochar which gave a sharp peak at 340 °C in the DTG curves was suggested as the reason that led to the increment in energy content.
In Australia, droughts are recurring events that tremendously affect environmental, agricultural and socio-economic activities. Southern Queensland is one of the most drought-prone regions in Australia. Consequently, a comprehensive drought vulnerability mapping is essential to generate a drought vulnerability map that can help develop and implement drought mitigation strategies. The study aimed to prepare a comprehensive drought vulnerability map that combines drought categories using geospatial techniques and to assess the spatial extent of the vulnerability of droughts in southern Queensland. A total of 14 drought-influencing criteria were selected for three drought categories, specifically, meteorological, hydrological and agricultural. The specific criteria spatial layers were prepared and weighted using the fuzzy analytical hierarchy process. Individual categories of drought vulnerability maps were prepared from their specific indices. Finally, the overall drought vulnerability map was generated by combining the indices using spatial analysis. Results revealed that approximately 79.60% of the southern Queensland region is moderately to extremely vulnerable to drought. The findings of this study were validated successfully through the receiver operating characteristics curve (ROC) and the area under the curve (AUC) approach using previous historical drought records. Results can be helpful for decision makers to develop and apply proactive drought mitigation strategies.
Sustainable agriculture is important for preserving environmental health and simultaneously gaining economic profits while maintaining social and economic equity. One way to evaluate sustainable agriculture is by studying agricultural eco-efficiency (AEE). Hence, this study constructed a data-driven method to evaluate and optimize AEE with the aim of providing a basis for improving the sustainable development of regional agriculture. Sixteen cities in Anhui Province, China, were considered in the study, and the variables used were agricultural resource inputs, environmental pollution, and agricultural economic development. Agricultural non-point source pollution (NPSP) emissions were considered the undesired output to build an AEE evaluation index system. Furthermore, a data envelopment analysis (DEA) model was established to analyse AEE from the static and dynamic perspectives. The spatial development and the temporal and spatial characteristics of AEE were also analysed. In addition, we applied a random effect (RE) panel Tobit model to quantitatively analyse the influencing factors of AEE from the input perspective and then proposed reasonable suggestions for improving the sustainable development of regional agriculture. Our findings show that the overall agricultural development in the 16 cities in Anhui Province has been continuously improving, even though there is an agglomeration of spatial development in some regions. In conclusion, this study provides suggestions and references for policy makers and agricultural practitioners regarding how to improve regional AEE and promote the sustainable development of the regional agricultural economy.
Large-scale national and transnational commercial land transactions, or Large-Scale Land Acquisitions (LSLAs), have been gaining a lot of academic attention since the late 2000s and since the reported rush for land, resulting in turn from an increase in demand for arable land. If many data exist to characterize land deals, the analysis of investment networks remain limited and predominantly portrays power asymmetries between countries from the Global North investing in the Global South. The aim of this work is to perform a deeper investigation on the land trade market, specifically focusing on cases that do not follow such narratives. For instance, almost 25% of the countries included in the transnational land trade network do not follow a strict investor/target dichotomy, thus being characterized by a double role, i.e., they both acquire and cede land in the transnational context. In order to globally acknowledge for what was currently considered as abnormal cases, we model open access data about LSLAs extracted from the Land Matrix Initiative (LMI) open-access database into a network graph, and adapt an eigenvector based centrality method originally conceived for online social networks, namely LurkerRank, to identify and rank anomalous profiles in the land trade market. We take into account three different network snapshots: a multi-sector network (including all the transnational deals in the LMI database), and three networks referring to specific investment sectors (agriculture,mines and biofuels). Experimental results show that emerging economies (e.g., China and Malaysia) play a central role in the land trade market, by creating alternative dynamics that escape the classic North/South one. Our analyses also show how African countries that are often seen as targets of land trade transactions in a specific sector, may often acquire foreign land in the context of investments in the same sector (i.e., Zimbabwe for biofuels and the Democratic Republic of Congo for the mining sector).
In today's fast-shifting climate change scenario, crops are exposed to environmental pressures, abiotic and biotic stress. Hence, these will affect the production of agricultural products and give rise to a worldwide economic crisis. The increase in world population has exacerbated the situation with increasing food demand. The use of chemical agents is no longer recommended due to adverse effects towards the environment and health. Biocontrol agents (BCAs) and biostimulants, are feasible options for dealing with yield losses induced by plant stresses, which are becoming more intense due to climate change. BCAs and biostimulants have been recommended due to their dual action in reducing both stresses simultaneously. Although protection against biotic stresses falls outside the generally accepted definition of biostimulant, some microbial and non-microbial biostimulants possess the biocontrol function, which helps reduce biotic pressure on crops. The application of synergisms using BCAs and biostimulants to control crop stresses is rarely explored. Currently, a combined application using both agents offer a great alternative to increase the yield and growth of crops while managing stresses. This article provides an overview of crop stresses and plant stress responses, a general knowledge on synergism, mathematical modelling used for synergy evaluation and type of in vitro and in vivo synergy testing, as well as the application of synergism using BCAs and biostimulants in reducing crop stresses. This review will facilitate an understanding of the combined effect of both agents on improving crop yield and growth and reducing stress while also providing an eco-friendly alternative to agroecosystems.
Studies reveal that climate change (CC) has higher negative impacts on agricultural production than positive impacts. Therefore, this article attempts to explore the impacts of CC on oil palm production in Malaysia and provides mitigation and adaptation strategies towards reducing such impacts. The multiple regression analysis is applied to assess the impacts of CC on oil palm production by using time series data in the period of 1980 to 2010. A negative and significant relationship is found between annual average temperature and oil palm production. If temperature rises by 1 °C, 2 °C, 3 °C, and 4 °C, production of oil palm can decrease from a range of 10 to 41%. This article has also found a negative impact of sea level rise (SLR) on oil palm production. Findings reveal that if areas under oil palm production decrease by 2%, 4%, and 8% due to SLR of 0.5, 1, and 2 m, oil palm production can decrease by 1.98%, 3.96%, and 7.92%, respectively, indicating that CC has a significant impact on the reduction of oil palm production in Malaysia, ultimately affecting the sustainability of oil palm sector in Malaysia. Finally, this study suggests to practice appropriate mitigation and adaptation strategies, including promotion and development of climate resilient varieties, soil and water conservation, afforestation, insurance and other risk transfer mechanisms, emission reduction technology, protection of coastal flooding for reducing the impacts of CC on oil palm production.
As a means of enhancing food security, efficient agricultural processing and the maintenance of a smooth supply chain are essential for ensuring food quality and reducing food wastage. Agricultural enterprises play a crucial role in the processing and transportation of food from farms to dinner tables. Operating income growth plays the vital role of ensuring that agricultural enterprises function in a stable manner while also indicating the quantity and quality of market food supply. Therefore, the objective of this study is to explore the impact of digital inclusive finance on food security by analyzing the effect of digital inclusive finance on the operating income of agricultural enterprises in China. By applying pooled OLS analysis to Chinese agricultural enterprises that are listed in the National Equities Exchange and Quotations, this study finds that digital inclusive finance can help improve agricultural operating income. The results reveal that digital inclusive finance can facilitate the promotion of agricultural operating income by increasing the supply of financing, accelerating inventory liquidity, and supporting investment in research and development. In addition, this study concludes that digital inclusive finance is more effective for increasing agricultural operating income as a result of its wider coverage and deeper utilization. Furthermore, the development of traditional finance is still necessary for the digitization of digital inclusive finance to be effective.
Unpredictable natural disasters, disease outbreaks, climate change, pollution, and war constantly threaten food crop production. Smart and precision farming encourages using information or data obtained by using advanced technology (sensors, AI, and IoT) to improve decision-making in agriculture and achieve high productivity. For instance, weather prediction, nutrient information, pollutant assessment, and pathogen determination can be made with the help of new analytical and bioanalytical methods, demonstrating the potential for societal impact such as environmental, agricultural, and food science. As a rising technology, biosensors can be a potential tool to promote smart and precision farming in developing and underdeveloped countries. This review emphasizes the role of on-field, in vivo, and wearable biosensors in smart and precision farming, especially those biosensing systems that have proven with suitably complex and analytically challenging samples. The development of various agricultural biosensors in the past five years that fulfill market requirements such as portability, low cost, long-term stability, user-friendliness, rapidity, and on-site monitoring will be reviewed. The challenges and prospects for developing IoT and AI-integrated biosensors to increase crop yield and advance sustainable agriculture will be discussed. Using biosensors in smart and precision farming would ensure food security and revenue for farming communities.
In the present study, an assessment of land suitability potential for agriculture in the study area of IBB governorate, Republic of Yemen has been conducted through close examination of the indicators of land characteristics and qualities. The objective of this study is to evaluate the available land resource and produce the potential map of the study area. Remote sensing data help in mapping land resources, especially in mountainous areas where accessibility is limited. Satellite imagery data used for this study includes data from multi-temporal Landsat TM which dated June 2001. The parameters taken into consideration were 16 thematic maps i.e., slope, DEM, rainfall, soil, land use, land degradation as well as land characteristics maps. Satellite image of the study area has been classified for land use, land degradation and soil maps preparation, while topo sheet and ancillary data have been used for slope and DEM maps and soil properties determination. The land potential of the study area was categorized as very high, high, moderate, low and very low by adopting the logical criteria. These categories were arrived at by integrating the various layers with corresponding weights in a Geographical Information System (GIS). The study demonstrates that the study area can be categorized into spatially distributed agriculture potential zones based on the soil properties, terrain characteristics and analyzing present land use. This approach has the potential as a useful tool for guiding policy decision on sustainable land resource management.