Agricultural Land Research Articles

Supervised land cover classification of Nueva Ecija using random forest in google earth

Land cover classification is essential for environmental monitoring, urban planning, and sustainable land use management. This study presents a supervised land cover classification in Nueva Ecija, Philippines, utilizing Google Earth Engine (GEE) and the Random Forest (RF) algorithm, applied to Landsat 8 imagery. A total of 1,523 samples were collected representing five land cover types: built-up areas, agricultural lands, water bodies, forests, and barren land. The classification achieved an overall accuracy of 87.69% with a Kappa coefficient of 0.841. Future work should explore the integration of seasonal imagery and topographic indices for improved performance. This methodology provides crucial insights for resource management and supports regional policy development in Nueva Ecija.

Long-distance hand tractor controlled by android system in Wetland Field

Modernization of agricultural practices is essential to overcome challenges such as decreasing agricultural labor and decreasing agricultural land in Indonesia. This research examines the development and performance of a hand tractor that is controlled remotely via an Android-based system using the NodeMCU ESP8266 module and a WiFi network. This study focuses on the technical and operational performance of hand tractors in wetlands. Modifications were made to the tractor, by installing three DC motors to control the clutch and gas levers. Tests were carried out on the control system accuracy and response time to assess the system's suitability for field operations. The research results show that the Android system has 100% accuracy in executing all commands given, but the response time decreases as the distance between the Android device and the WiFi module increases, with an average delay reaching 6,8 seconds for a maximum 100 m distance. Field efficiency testing shows that a tractor with Android control has a work efficiency of 70.48% in the plowing process and 75.33% in the harrowing process, lower than with manual control which reaches an efficiency of more than 95%. Despite response delays and reduced efficiency, the system is an important step toward agricultural mechanization, especially in labor-intensive wetlands.

A novel machine learning-driven approach for predicting nitrous oxide flux in precision managed agricultural systems

N2O, also known as nitrous oxide, is a greenhouse gas that is roughly 300 times more potent than CO2 and destroys the the stratospheric ozone layer causing climate change. One of the primary causes of the rapid increase of N2O in our ecosystem is the application of nitrogen fertilizer to agricultural land. This stimulates N2O emissions and accounts for approximately 5% of the global greenhouse gases, forcing harm to the environment and atmosphere (Aronson and Allison, 2012). Previous models severely underestimated N2O flux in various crops, causing inaccurate predictions to form. In our study, we utilized data from automated flux chambers to train and evaluate different machine-learning models to predict the field-level flux of N2O which assist farmers to predict fertilizer amounts to use. The best machine learning model, Random Forest, performed considerably better than the standard empirical and biophysical models by roughly 15%, and show promise in improving predictive accuracy and guiding sustainable agricultural practices.

Achieving land degradation neutrality: land-use planning and ecosystem approach

IntroductionThe research purpose is to scientifically substantiate an integrated approach to solving the problem of land degradation, based on the idea of land degradation neutrality (LDN), taking into account ecosystem services when planning land use to maximize the conservation of natural capital. The methodological basis of the research is the provisions and principles of the concepts of sustainable development, achieving LDN, and ecosystem services, as well as the research results revealing various aspects of land use, particularly their degradation.MethodsThe following research methods are used in the paper: dialectical – to determine the cause-and-effect conditions of land degradation; analysis – to highlight the current state of land use in Ukraine and the factors that have led to land degradation; synthesis – for global trends towards achieving LDN; deduction – to explore the possibility of introducing global experience in achieving LDN in Ukraine; structural-functional analysis – to substantiate the feasibility of introducing an ecosystem approach to land-use planning to achieve LDN.ResultsAs a result of the research, the current land degradation state in Ukraine has been analyzed, and ways of achieving LDN in land-use planning through the prism of an ecosystem approach have been substantiated. Based on statistical data, the current and potential levels of arability of the territory of Ukraine have been calculated by natural-climatic zones, and the areas of eroded arable lands in Ukraine have been determined by the erodibility factor (low-eroded, mediumeroded,and highly-eroded).DiscussionFor the first time, a structural-logical scheme has been developed for organizational-economic support for the effective use of degraded and low-productive agricultural lands in the context of implementing the idea of LDN, which is a tool for rational allocation and use of degraded lands. This scheme can serve as a basis for the development of land-use planning strategies for territorial communities, for institutions, and organizations competent in the field of land management.

Declining interest in afforestation under the common agricultural policy. Evidence from Poland and Lithuania

Land afforestation is an important aspect of forested land development. Increasing the area of forest areas through the reforestation of uncultivated, abandoned or agriculturally unsuitable land is considered an important way to diversify economic activities in order to reduce dependence on agricultural activities and improve environmental conditions in rural areas. The main objective of the study is to identify the factors affecting the afforestation of agricultural land carried out in the years 2004–2020 by farmers under the individual financial perspectives of the Rural Development Programme (RDP) in Poland and Lithuania. The study included a review of Polish and Lithuanian regulations aimed at providing financial support for afforestation under the RDP. Moreover, a comparative analysis of the rules and criteria for financial support for afforestation in relation to selected socio-economic indicators of the two countries was carried out. Based on the study results, it can be clearly stated that in both Poland and Lithuania, the support for afforestation under the RDP fails to meet the beneficiaries’ expectations. It would, therefore, be advisable to adapt the Programme to the changing economic conditions and keep the afforested land under the RDP under technical supervision. Support for afforestation should be continued to ensure the improvement in land use and the enhancement of the prospects for long-term economic activity in rural areas as well as to implement the assumptions of the green economy.

Modeling the seasonal wildfire cycle and its possible effects on the distribution of focal species in Kermanshah Province, western Iran.

Predicting environmental disturbances and evaluating their potential impacts on the habitats of various plant and animal species is a suitable strategy for guiding conservation efforts. Wildfires are a type of disturbance that can affect many aspects of an ecosystem and its species. Therefore, through the integration of spatial models and species distribution models (SDMs), we can make informed predictions of the occurrence of such phenomena and their potential impacts. This study focused on five focal species, namely, the brown bear (Ursus arctos), wild goat (Capra aegagrus), wild sheep (Ovis orientalis), wildcat (Felis silvestris), and striped hyena (Hyaena hyaena). This study used MODIS active fire data and ensemble machine learning methods to model the risk of wildfire occurrence in 2023 for spring, summer, and autumn separately. This study also investigated the suitability of habitats for focal species via SDMs. The predicted probability maps for wildfire risk and habitat suitability were converted to binary values via the true skill statistic (TSS) threshold. The overlap of the habitat suitability map and wildfire occurrence areas was analyzed via GAP analysis. The area prone to fire in spring, summer and winter is equal to 9077.32; 10,199.83 and 13,723.49 KM2 were calculated, which indicates an increase in wildfire risk. Proximity to roads is one of the most important factors affecting the possible effects of wildfires in all seasons. Most fire occurrences are concentrated on agricultural lands, which, when integrated with other land use types, have wildfire potential in all seasons. The use of fire to destroy agricultural residues is a critical factor in the occurrence of wildfires. The distribution range of each focal species is considered the most important component of fire susceptibility. Hence, the suitable habitat for Hyaena hyaena in spring, summer, and autumn, with areas of 5.257, 5.856, and 6.889 km2 respectively, is the most affected by the possibility of fire. In contrast, these areas have the lowest values for Ovis orientalis, with 162, 127, and 396 km2 respectively. Therefore, species that are dependent on human-based ecosystems have the highest vulnerability to wildfire. Conservation efforts should focus on familiarizing farmers with methods of destroying agricultural residues as well as the consequences of intentional fires. The findings of this study can be used to mitigate the negative impacts of wildfire and protect the habitat of focal species.

Effect of climate change on West Nile virus transmission in Italy: a systematic review

Abstract Background West Nile Virus (WNV) is a pathogen transmitted by mosquitoes of the Culex genus, affecting both humans and animals such as horses and birds. The prevalence of WNV infection has increased dramatically in recent years in Europe, especially in Italy, and has been related to climate and environmental changes linked to global warming. Our review aims to assess the relation between the spread of WNV in Italy and the different climate change-related factors. Methods We conducted a literature search in the PubMed, Web of Science, Embase and Scopus databases, actualised as of 20 February 2024, using as search terms WNV, its vectors and climate change, and limiting our analysis to studies conducted in to avoid an overly heterogeneous setting. We registered the review in PROSPERO (CRD42023430636). Results Of the 252 unique records retrieved, we eventually included 25 articles after full-text screening. These studies were published between 2011 and 2023 and evaluated the distribution of WNV or its vectors according to various climatic and environmental factors. Despite rather heterogeneous results, we found a consistently positive association with temperature, especially spring temperature, agricultural land use, demography, and soil moisture, and a negative one with evapotranspiration and wind, while the relationship with other environmental variables and water cycle-related factors was weak, null or conflicting. Conclusions Despite some inconsistencies in the results, likely related to differences in study methodologies as well as complex environmental interactions, we found unequivocal evidence that climate change-related factors, such as the lengthening of the warm season, extensive farming, and invasive human activities towards natural environments, favour WNV spread in Italy. Consequently, actions against global warming and preservation of natural environments may counteract the WNV epidemic. Key messages • WNV epidemic seems to be linked to specific climate change-related factors, such as the lengthening of the warm season, extensive farming, and invasive human activities towards natural environments. • Actions against global warming and preservation of natural environments may counteract WNV spread.

TRANSFORMACIONES EN LA CONFIGURACIÓN Y FUNCIONALIDADES DE LA PERIFERIA URBANA NOROESTE DE LA GRAN ÁREA METROPOLITANA DE COSTA RICA (GAM)

The peripheries are generally conceived as scenarios for expanding various economic activities in central cities, and their land uses usually explain both their functionality and territorial organization. However, the literature shows that the logic that configures these places also goes through a framework of territorialized human relations and that their forms of organization are also linked to social valuations. Given the above, the research emphasizes the importance of taking a comprehensive look, based on spatial structures, to understand the changes and contemporary functionalities of the periphery under study. For this, a qualitative methodology of an ethnographic type was considered, based on observing land uses and the practices of the different users. As a result, the spatial structures approach made evident other activities and relationships between society and the environment, such as forms of recreation on agricultural land, the agriculturization process, agro-productive diversification, and changes in residential models, which must be taken into account for current and future planning of the periphery. Keywords: Land Use, Transformations in Urban Peripheries, Functionalities, Geographic Space, Social Valuation, Gran Área Metropolitana.

Remote sensing and machine learning algorithms to predict soil salinity in southern Kazakhstan

Salinization and land degradation are significant challenges in the southern regions of Kazakhstan. These issues arise due to climate change, unequal water resource distribution, and human impact. The primary concern revolves around water resources, which are influenced by the area’s trans boundary flow of major rivers. The low level of water and food security has pushed the development of new approaches based on remote sensing monitoring and geographic information systems (GIS) to provide solutions for soil salinity. The research aims to focus on utilizing high-resolution radar images. This data type is effective for cloudy weather and can be useful for continued monitoring of some areas. Machine learning methods can solve the problem of automatic mapping of agricultural land salinity in Kazakhstan’s southern regions. The precise mapping of the salinity area helps prevent or decrease salinity’s impact on agriculture. The experiment realized that complex models such as LightGBM do not have significant accuracy performance over simple models on a small dataset compared with Ridge regression. The results allow us to recommend an approach for further improvement with ground-based measurement data and other deep-learning methods for mapping the salinity of agricultural lands.

Hydroponic Cultivation of Spices and Aromatics: Techniques, Benefits and Challenges

The global shift towards urbanization, with 55% of the population residing in urban areas and an anticipated rise to 68% by 2050, presents challenges to traditional farming. The increasing world population, further intensifies the strain on available agricultural land due to urban expansion. Climate change combines with these issues, make conventional cultivation more challenging. Alternative agricultural practices, such as hydroponics, gain significance in addressing these concerns. Aromatic and spices have been used for centuries for their medicinal and culinary properties. They contain various essential oils, flavonoids, and other phytochemicals responsible for their distinctive flavour and aroma, as well as their health benefits. The rising global demand for natural products from herbs and spices has led to its market growth. Hydroponics, a technology for cultivating plants in nutrient solutions with or without an artificial medium such as sand, gravel, vermiculite, rockwool, peat moss, coir or sawdust, offers a promising solution. This paper explores the potential of hydroponic cultivation for planting material production, enhancing the yield, nutritional composition, essential oil content and medicinal properties of various spice and aromatic crops, including black pepper, saffron, ginger, garlic, coriander, mint, celery, fenugreek, parsley, thyme, rosemary, green mustard, basil and others. The main challenges in hydroponic production of these crops include high initial investment, precise monitoring and lack of technical knowledge. Hence by reviewing recent literature, this paper aims to provide insights into the classification of hydroponics and its different techniques, benefits and challenges of hydroponic cultivation, various growing media and nutrient solutions used, types of hydroponics utilized and their purposes in the production of diverse herbs and spices. Embracing hydroponic techniques could contribute to sustainable agricultural practices, particularly in urbanized environments where traditional farming faces constraints.

Georgia’s potentials for sustainable intensification, increasing food security and rural incomes.

Abstract Increasing global demand for agricultural commodities spurs conversions of natural ecosystems. Sustainable intensification in areas of high yield gaps has been proposed a pathway to achieve food security, support rural livelihoods, while also reducing the impact of commodity production by narrowing yield gaps on existing agricultural lands, while improving resource efficiency. Following the dissolution of the Union of Soviet Socialist Republics (USSR), Georgia experienced one of the highest losses of agricultural productivity among all former USSR countries and is now highly dependent on food imports. Closing yield gaps in Georgia through sustainable intensification has the potential to increase food self-sufficiency, support rural livelihoods, and strengthen food security and sovereignty. We estimated its potential for sustainable intensification in current agricultural areas to achieve self-sufficiency for wheat, maize, and barley. We found that crop yields can be doubled to tripled under high-input production systems, using high-yielding varieties, optimized inputs, fertilizers, and pest control. Yet, self-sufficiency in wheat can only be reached if at least 80% of the potentially attainable yields are achieved and if land is strategically allocated between crops. To achieve such increases, farmers need access to and training for using different crop varieties, fertilizers, and pest and disease control practices and products. Intensification increases the risks to ecosystem services’ health and livelihoods, particularly raising equity concerns. Yet, intensifying very low input systems is often found to be more sustainable, with high yield increases compared to limited impacts on the environment. The high employment rate in the agricultural sector in Georgia particularly provides opportunities to reduce poverty and increase livelihoods through increasing incomes and food security.

Monsoonal impacts on the community trophic niches in two temperate headwater tributaries across a land use continuum

Pulsed flows following heavy monsoon rain events alter riverine food webs, but their impact on headwater stream food webs across the continuum from forested canopy to open agricultural land use remains unclear. We investigated carbon and nitrogen stable isotopes in macroinvertebrates and fish in two tributaries of the Suyeung River, Korea, before and after heavy monsoon rains to assess changes in community trophic niches. Basal resources (leaf litter and biofilms) exhibited consistent δ13C and δ15N values across seasons, with biofilms showing higher δ13C values. δ15N values increased from forested to agricultural reaches, indicating varied nutrient inputs. Consumer isotope values remained stable over time but varied longitudinally, reflecting reliance on local resources. Trophic niches differed between watershed locations but overlapped seasonally. Despite a decrease in consumer δ13C ranges after heavy rainfalls, variations in their δ15N ranges and the ellipse centroid (SEAc) of isotopic niches between sites resulted in broadly consistent SEAc across locations and seasons. This indicates limited evidence for directional reshaping of food-web properties across channel reaches following monsoon rains. Downstream isotopic shifts suggest substantial agricultural influences on food webs. Overall, our findings highlight that monsoon rains may have minimal effects on the community trophic niches of stream food webs.

EFFICIENT SCHEMES OF CLASSIFIERS FOR REMOTE SENSING SATELLITE IMAGERIES OF LAND USE PATTERN CLASSIFICATIONS

Land use pattern classification of remote sensing imagery data is imperative to research that is used in remote sensing applications. Remote sensing (RS) technologies were exploited to mine some of the significant spatially variable factors, such as land cover and land use (LCLU), from satellite images of remote arid areas in Karnataka, India. Four diverse classification techniques unsupervised, and supervised (Maximum likelihood, Mahalnobis Distance, and Minimum Distance) are applied in Bellary district in Karnataka, India for the classification of the raw satellite images. The developed maps are then visually compared with each other and accuracy evaluations make using of ground-truths are carried out. It was initiated that the Maximum likelihood technique gave the finest results and both Minimum distance and Mahalnobis distance methods overvalued agricultural land areas. In spite of missing a few insignificant features due to the low resolution of the satellite images, a high-quality accord between parameters extracted automatically from the developed maps and field observations was found. Keywords: Remote sensing (RS), land cover and land use (LCLU).

Evaluation of Land Suitability for Organic Horticulture Farming in Support of Sustainable Agrarian Governance

Abstract: Land compatibility can contribute to the optimization of agricultural land use, because this evaluation is used to determine the most appropriate spatial plan for current and future land use. Identifying the suitability of agricultural land is necessary to adapt to increasing food needs caused by expanding population, environmental pollution and climate change. Therefore, this research was conducted to assess the suitability of snake fruit plants. Various factors were considered in this research, such as rainfall, temperature, nutrient availability (wa), rainfall density (rc), organic carbon (nr), slope, flood hazard (fh) and erosion hazard (eh). The results of the classification of land suitability classes in the Sleman Regency area, in this case, are that snake fruit plants in most areas fall into the criteria for land suitability class S2 or quite suitable. The results are with an area of??42,150 Ha or with a percentage of 73.4% of the land suitability class S2 or Sufficiently Suitable. (Medium Suitable) is land that has quite heavy barriers to maintain the level of management that must be carried out. In the land suitability class S1 or very suitable in the research area with an area of?? 10625 Ha or with a percentage of 18.5, the land suitability class Very Suitable (Very Suitable) is land that has no boundaries. The results of the land suitability assessment, both actual and potential, can be carried out by improving efforts such as improving drainage, adding organic material, planting according to contours, fertilizing. Keywords: Horticulture, Uniformity, Land Suitability, Organic Farming, Snake Fruit

Acidity and Salinization of Soil Following the Application of Ashes from Biomass Combustion Under Different Crop Plant Species Cultivation

Ashes from biomass combustion (BAs) are a valuable source of plant nutrients, making them suitable for fertilizing crops. BAs also contain components that directly affect soil environmental conditions, leading to improved growth and development of plants. Their deacidifying properties allow BAs to serve as a substitute for calcium fertilizers. However, they contain substantial amounts of components that can increase soil salinity, which can have negative effects. The aim of this study was to assess the impact of BAs on changes in pH and salinity of haplicluvisol soil under the cultivation of various plant species. The study also analyzed the effects of BAs on the content of total forms of calcium (Ca), magnesium (Mg), potassium (K), and sodium (Na) in the soil. The BAs used in the experiment were sourced from a combined heat and power plant that combusts forest and agricultural biomass. These BAs are distributed as a product for fertilizing agricultural land. However, their application is not subjected to further monitoring. The results indicated that the application of different doses of BAs significantly affected the pH of the analyzed soil. Compared to control objects, a significant increase in pH was observed, with these changes dependent on the species of the cultivated plant. Additionally, even the smallest doses of BAs caused an increase in the electrolytic conductivity (EC) of soil solutions, which serves as a measure of soil salinity. Despite the increase in the average EC value, the application of BAs did not alter the salinity class. The use of BAs also significantly influenced other analyzed parameters. An increase in the average content of total forms of Ca, Mg, K, and Na in the soil was observed, along with a higher degree of soil saturation with alkaline cations, compared to the control and the soil condition before the experiment. The changes in the analyzed soil parameters were significantly influenced not only by the different doses of BAs but also by the species of the cultivated plant. The greatest fluctuations in the obtained values were observed in soil under winter rape cultivation, while the smallest fluctuations were noted in soil under spring barley and potato cultivation.

Examining the impact of water scarcity on agricultural output in the Gulf Cooperation Council (GCC) region

AbstractThis research paper examines the impact of water scarcity on agricultural output in the Gulf Cooperation Council (GCC) region. It highlights the increasing global water scarcity and its consequences, including the prevalence of water‐stressed areas and the adverse effects on human health and economic sectors. The paper focuses on the GCC region, known for its water scarcity and desertification, exacerbated by climate change and pollution. The study aims to investigate the relationship between water stress levels and agricultural productivity, evaluating the effects of freshwater withdrawals, fertilizer consumption, and agricultural methane emissions on crop production. Additionally, it explores the connection between agricultural output and net national income per capita. By addressing these research questions, the study contributes to the understanding of agricultural water scarcity and its implications for global food security and sustainability. The paper utilizes data from the World Bank to analyze crop production in the GCC countries from 2001 to 2020, shedding light on the influence of water scarcity on agricultural landscapes. The research highlights the efforts made by GCC countries to enhance food security, including strategic investments in agricultural lands and bilateral agreements. It also discusses the challenges faced by specific countries within the region, such as Saudi Arabia, the United Arab Emirates (UAE), and Kuwait, in terms of agricultural production and water constraints. Overall, the paper provides insights into the complexities of agricultural water scarcity and its effects on the GCC region, emphasizing the need for sustainable solutions to ensure food security and economic stability.

Family farm and family business: Distinction and transition between concepts

Objective: Explore the conceptual limits between the family farming unit and the agricultural family business to identify the elements that determine the transition from one figure to another. Methodology: The distinctive elements between both concepts were determined from the most relevant definitions reported in the literature. Subsequently, the transition from a family farming unit to an agricultural family business was illustrated based on the case study of an enterprise dedicated to the production and collection of barley in Hidalgo, Mexico. Results: Seven distinctive elements were identified between the family farming unit and the agricultural family business. Two elements (the scale of production and productive diversification) drive the transition, three are modified consequently (the source of labor, the priority destination of productive activity, and the strength of the link with agricultural land), and the remaining two are carried out infrequently (assignment of a non-family member as main head and disengagement of family members from community life). Limitations: The findings of the study lack statistical representativeness as they come from a case study. Conclusions: The seven distinctive elements between a family farming unit and an agricultural family business can be used to study the phenomenon of entrepreneurship around agriculture and to typify agricultural units to design differentiated strategies for political intervention.

Assessment of Nitrate Reduction by Microbes in Artificial Groundwater Medium

There are significant reasons for nitrate contamination in groundwater (Delhi, India): sewage, runoff from landfill sites, nitrogenous chemical fertilisers, and pesticides from agricultural lands. The highest recorded concentration of nitrate in Delhi’s groundwater is reported to be 1500 mg/l. Consumption of high nitrate through water may pose serious health problems in humans, especially children (below five years). The study’s primary objective was to isolate and identify nitrate-remediating microbes from the nitrate-contaminated site Okhla Barrage, located on the Yamuna River in Delhi, India. A total of 11 different strains were isolated from this site. Among these four strains exhibited 40%–50% remediation efficiency at a nitrate concentration of 1000 mg/l. Molecular characterisation revealed that these four strains, Enterobacter aerogenes, E. coli K12, <i>Klebsiella oxytoca</i> and <i>Lelliottia amnigena</i>, belong to the Enterobacteriaceae family. This study assessed the nitrate remediation potential of isolated microbes in groundwater with 1000 and 1500 mg/l nitrate concentrations. By using a 2% inoculum, the microbes were incubated anaerobically at room temperature for ten days. Nitrate concentrations were monitored every 48 hours. <i>Lelliottia</i>, <i>E. coli</i>, and <i>Enterobacter</i> reduced nitrate (1500 mg/l) by approximately 42%, 24%, and 29%, respectively, while <i>K. oxytoca</i> showed minimal reduction. <i>L. amnigena</i> exhibited superior nitrate removal efficiency compared to other strains. According to the reported data, these strains are known to reduce nitrate concentrations of 620 mg/l. However, our findings demonstrate a remarkable nitrate remediation capacity of 1500 mg/l, showcasing a novel contribution to this study. Further detailed analysis for condition optimisation and association of microbe-microbe could be more helpful.

Investigating the sustainable energy generation potential of an invasive weed: Lantana camara.

Lantana camara, one of the world's top ten most invasive species, was initially cultivated for ornamental use. However, it spread uncontrollably across the fallow areas and agricultural lands, threatening approximately 44% of Indian forests. Its invasion disrupts ecosystems by suppressing nearby plant growth through allelopathy and poses toxicity risks to grazing ruminants. It significantly increases forest fire risk by adding large amounts of combustible biomass, particularly dried L. camara. Despite efforts to control it using mechanical, chemical, and biocontrol methods, the results have been largely unsatisfactory, with associated costs estimated at $18,000 per square kilometre. Considering these challenges, recent research explored the potential of L. camara as a bioenergy resource. TheL. camarabriquettes exhibit a heating value of approximately 20MJ/kg with a low sulphur (0.5%), nitrogen (1%), and ash content (2%), making them suitable for decentralised energy production. Furthermore, bioethanol production fromL. camarahydrolysate has shown promising results, yielding 0.33g/g withPichia stipitisand0.47g/g with Saccharomyces cerevisiae, which is comparable to other lignocellulosic feedstocks. Additionally, the gasification of L. camara using a downdraft gasifier produced syngas with a lower heating value (LHV) of 6.4MJ/Nm3. These findings demonstrate that using L. camara for bioenergy production presents a dual solution, addressing the growing demand for renewable energy and managing invasive species. This review aims to critically evaluate the potential and challenges associated with the different energy production pathways for L. camara, highlighting its role in sustainable energy generation.

Short‐Term Grasslands in Agriculture Support Both Natural Enemy and Phytophagous Arthropod Populations

ABSTRACTSimplified agricultural land with high chemical input is increasingly replacing natural habitats in many parts of the world. This loss and fragmentation of natural areas may have profound impacts on an array of wildlife, including predatory arthropods that provide natural biocontrol of agricultural pests. There is increasing interest in utilising short‐term seminatural habitat restoration in agricultural systems to support populations of beneficial organisms. We assessed the impact of two types of short‐term set‐asides on the diversity and abundance/activity density of phytophagous and predatory arthropods. We found that flower‐enhanced set‐asides supported higher abundances of both phytophagous and predatory arthropods compared to control crop fields. In addition, we found that both set‐aside types (flower enhanced and traditional) supported a more active and diverse community of beneficial predatory carabid beetles, with particularly strong trends in set‐asides supplemented with flowering plants. These results suggest that small‐scale, temporary, seminatural habitats in agricultural systems provide support for a diverse and abundant community of both beneficial and phytophagous arthropods.