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This study assesses the extent to which growers integrate the 13 principles of agroecology within their farm activities. This is a first step that helps to determine the changes required in different farm components to foster the adoption of agroecology. We used the holistic Farm-Level Agroecology Criteria Tool (F-ACT) to assess the alignment with agroecological principles of 31 farms representative of smallholder irrigated family farms in the Merguellil Plain region of central Tunisia. Although various agroecological practices were implemented, the results revealed little integration of agroecological principles at the farm level, estimated at 34% of the full transition. In a difficult production resource access context, recycling and reducing inputs were the principles mostly applied, with the goal of improving resource use efficiency and farm system resilience. More specialized and market-oriented farms tended to rely on conventional practices, while diversified farms, often cultivating their own land, exhibited more sustainable practices. Although producers’ dominant rationale was to improve their market access, thereby often resorting to more conventional practices, we nevertheless noted interesting practices such as mixed crop and livestock production, which could facilitate the transition to more agroecological systems in these lowlands where production factors are scarce. [ABSTRACT FROM AUTHOR]

Dairy farming is a notable source of nitrogen (N) emissions, impacting both atmospheric and aquatic ecosystems, thus necessitating a detailed analysis of nutrient dynamics to curtail nutrient wastage. However, N flow variability and its environmental ramifications differ markedly among dairy farms, and a holistic understanding of these differences is lacking in Inner Mongolia, the biggest dairy production province in China. Utilizing data from 187 dairy farms and employing the NUFER-farm model, this study assessed N flows, N use efficiency (NUE), and N losses across four predominant dairy farming systems in Inner Mongolia. These systems include traditional pastoral dairy farms (PF), smallholder dairy farms with croplands (SF), industrial landless farms (IDF), and coupled dairy cattle and cropland-intensive farms (CDF). Our findings indicate considerable differences in N flows, NUE, and losses among the systems. On average, N deposition and N fertilizer were the primary N sources for PF and SF, respectively, whereas IDF and CDF derived over 90% of their N inputs from purchased feeds. PF and SF recycled all available manure N on-farm, whereas IDF and CDF recycled only approximately 36% of the total available manure N. N losses constituted 39-72% of total N outputs, with ammonia emissions accounting for 68-73% of total N losses across all farm types. In particular, PF had a higher N loss per kilogram of dairy product than other systems. Farm-level NUE ranged from 17 to 35%, with manure management practices showing significant variability, underscoring the potential for enhanced strategies to reduce N losses through improved manure treatment. [ABSTRACT FROM AUTHOR]

Fast and accurate anomaly detection is critical in telemetry systems because it helps operators take appropriate actions in response to abnormal behaviours. However, recent techniques are accurate but not fast enough to deal with real-time data. There is a need to reduce the anomaly detection time, which motivates us to propose two new algorithms called AnDePeD (Anomaly Detector on Periodic Data) and AnDePed Pro. The novelty of the proposed algorithms lies in exploiting the periodic nature of data in anomaly detection. Our proposed algorithms apply a variational mode decomposition technique to find and extract periodic components from the original data before using Long Short-Term Memory neural networks to detect anomalies in the remainder time series. Furthermore, our methods include advanced techniques to eliminate prediction errors and automatically tune operational parameters. Extensive numerical results show that the proposed algorithms achieve comparable performance in terms of Precision, Recall, F-score, and MCC metrics while outperforming most of the state-of-the-art anomaly detection approaches in terms of initialisation delay and detection delay, which is favourable for practical applications. [ABSTRACT FROM AUTHOR]

Mycorrhizal fungi, which form symbiotic associations with plant roots, can be critical to the successful establishment of plants. Up to 90% of land plant species form associations with arbuscular mycorrhizal fungi (AMF), which develop fungal structures inside host roots. Common site preparation practices prior to ecological restoration, such as soil disturbance and herbicide application, may lessen the availability of AMF, potentially jeopardising plant establishment. However, the state of the soil microbiome can be difficult to assess directly. This study examines the potential of the widespread pasture grass Anthoxanthum odoratum (Sweet Vernal, Poaceae) as a bioassay of AMF abundance in soils. We present a rapid clearing and staining protocol for visualising AMF structures in the roots of A. odoratum. We then compare % colonisation on roots of naturally established A. odoratum plants collected from undisturbed pastures vs. sprayed and tilled pastures. Roots of A. odoratum collected from sprayed and tilled pastures showed a significantly lower average AMF colonisation at 43.8% compared with 57.6% on roots from undisturbed pastures. However, all plants sourced from the sprayed and tilled pastures still showed some AMF structures. Our results strongly suggest that A. odoratum can be a useful bioindicator of AMF presence and abundance in soils, offering a promising tool for seed-based restoration on retired agricultural land. [ABSTRACT FROM AUTHOR]

This study evaluated the responses of sweet potatoes to Cadmium (Cd) stress through pot experiments to theoretically substantiate their comprehensive applications in Cd-polluted agricultural land. The experiments included a CK treatment and three Cd stress treatments with 3, 30, and 150 mg/kg concentrations, respectively. We analyzed specified indicators of sweet potato at different growth periods, such as the individual plant growth, photosynthesis, antioxidant capacity, and carbohydrate Cd accumulation distribution. On this basis, the characteristics of the plant carbon metabolism in response to Cd stress throughout the growth cycle were explored. The results showed that T2 and T3 treatments inhibited the vine growth, leaf area expansion, stem diameter elongation, and tuberous root growth of sweet potato; notably, T3 treatment significantly increased the number of sweet potato branches. Under Cd stress, the synthesis of chlorophyll in sweet potato was significantly suppressed, and the Rubisco activity experienced significant reductions. With the increasing Cd concentration, the function of PS II was also affected. The soluble sugar content underwent no significant change in low Cd concentration treatments. In contrast, it decreased significantly under high Cd concentrations. Additionally, the tuberous root starch content decreased significantly with the increase in Cd concentration. Throughout the plant growth, the activity levels of catalase, peroxidase, and superoxide dismutase increased significantly in T2 and T3 treatments. By comparison, the superoxide dismutase activity in T1 treatment was significantly lower than that of CK. With the increasing application of Cd, its accumulation accordingly increased in various sweet potato organs. The the highest bioconcentration factor was detected in absorbing roots, while the tuberous roots had a lower bioconcentration factor and Cd accumulation. Moreover, the transfer factor from stem to petiole was the highest of the potato organs. These results demonstrated that sweet potatoes had a high Cd tolerance and a restoration potential for Cd-contaminated farmland. [ABSTRACT FROM AUTHOR]

Nigeria is the most populous country in Africa, and the essential foods for Nigerians are cereal crops, including maize, rice, sorghum, millet, and wheat. However, their productivity is significantly affected by population pressure, poor cropland utilization, and high fertilizer costs. Against this backdrop, this study examines the relationship between cereal production, cereal cropland area, fertilizer usage, and the rural population (farm labor). The study utilizes the novel dynamic autoregressive distributed lag simulation (DYARDLS) model and analyzes annual time series data for Nigeria from 1980 to 2021. The unit root test results suggest that the chosen variables are stationary. Furthermore, the bound test affirms that all variables are cointegrated, with a significance level of 1%. The results from the DYARDLS show that in the long run, a percentage change in rural population and cereal cropland area boosts cereal food production by 0.018% and 0.51%, respectively. Meanwhile, a 1% change in the food production index exacerbates cereal output by 0.25% in the long run and 1.06% in the short run. We also find that fertilizer consumption could improve cereal production in the short and long run, but the results are insignificant. In conclusion, we demonstrate that our study variables are the decisive determining factors of cereal productivity and cannot be disregarded in the mission to attain food security. [ABSTRACT FROM AUTHOR]

The identification of agricultural lands is crucial for sustainable development in rural areas. In this paper, an augmented model that utilizes multimodal satellite-based data samples for identifying agricultural lands via incremental learning. The Whale Optimization Algorithm (WOA) is used for augmenting collected images and data samples to enhance the accuracy of the model under real-time conditions. To identify agricultural lands, Deep Convolutional Neural Networks (CNNs) are trained on the augmented data samples. Additionally, the incorporation of Q-Learning for continuous optimization of the model to enhance its efficiency and effectiveness in identifying agricultural lands. The proposed model offers many edges over existing methods. Firstly, the use of multimodal satellite-based data samples allows for a comprehensive and accurate analysis of agricultural lands. Secondly, the incorporation of the Whale Optimization Algorithm enables the augmentation of collected data samples, leading to improved accuracy and reliability of the model. Thirdly, Deep CNNs allows the extraction of complex features from the data, leading to more accurate identification of agricultural lands. Finally, the use of Q-Learning ensures that the model is continuously optimized to improve its efficiency and effectiveness. The need for this work arises from the limitations of existing methods in accurately identifying agricultural lands. Traditional methods based on manual surveys and visual interpretation are time-consuming, expensive, and prone to errors. Moreover, existing automated methods often lack the ability to analyse multimodal satellite-based data samples and fail to provide accurate results. Based on these observations, the proposed augmented model offers a promising solution for identifying agricultural lands for sustainable development. The use of multimodal satellite-based data samples, WOA, Deep CNNs, and Q-Learning allows for an accurate and efficient analysis of agricultural lands, which can aid in sustainable development planning and decision-making operations. [ABSTRACT FROM AUTHOR]

Intensive agricultural land use negatively impacts biodiversity, including arthropod predator diversity and their pest control potential. Alley‐cropping agroforestry systems (integration of tree rows into arable land) are increasingly considered an economically viable alternative for more sustainable and biodiversity‐friendly agriculture. However, their effectiveness in promoting generalist predator diversity and pest control, especially as a function of space (distance from tree rows) and time (across the growing season) remains poorly understood. We assessed how spider abundance, taxonomic and functional diversity (as proxies of pest control potential) respond to temperate alley‐cropping agroforestry systems as compared to open croplands. Additionally, we analysed whether spiders with different habitat preferences (eurytopic, forest and open‐habitat specialists) show distinct responses. Lastly, we analysed whether the influence of agroforestry changes with decreasing tree proximity and across the growing season. Tree row proximity generally benefited the abundance, as well as taxonomic and functional diversity of the overall spider communities. Alley‐cropping promoted the abundance and taxonomic diversity of forest specialists and eurytopic spiders, without negatively affecting open‐habitat specialists. The positive effect of alley cropping was strongest within tree rows and their immediate vicinity, but was still detectable at considerably farther distances. These patterns were temporally dynamic with spider abundance and taxonomic diversity being highest within tree rows in early spring and spiders spilling over to adjacent crop rows in subsequent periods. Synthesis and applications: Overall, our findings highlight that the inclusion of tree rows benefited spiders across the entirety of the 48 m crop rows, independent of crop type. Since the peak in spider abundance and taxonomic diversity in crop rows coincided with crop pest arrival, alley‐cropping agroforestry is expected to benefit agricultural production through increased pest control potential. Alley‐cropping agroforestry may therefore be an important management strategy to develop more sustainable agricultural systems benefiting farmers and biodiversity alike. [ABSTRACT FROM AUTHOR]

Alternative food network research has referred to farm shops but rarely examined them in detail. This article argues that farm shops are not simply places of exchange but are complex, immersive, sensory‐rich places of meaning and experience. These arguments are developed through a study that adopts an interpretive/embodied perspective on conventions and applies them through a multi‐methods approach, incorporating autoethnography, interviews, a questionnaire and website analysis. The influence of farm shop interiors and farm context were investigated through thematic analysis. It was found that consumers desired a rustic interior aesthetic, an external ambience enabling exploration and an opportunity to escape time poverty through consumption. Farm shop owners endeavoured to provide a rich multi‐sensory interactive consumption experience, encompassing both enterprise buildings and surrounding farm sites. It is argued that use of 'farm‐site' consumption experiences distinguishes these enterprises from each other, contributes to consumer immersion and positively contributes to the collective identity of 'farm shops'. Furthermore, the provision of retroscaped experiential consumption promises resilience from supermarket co‐optation. [ABSTRACT FROM AUTHOR]

This study aimed to assess the potential of soil organic carbon (SOC) production through organic amendments. SOC sequestration would help to achieve the net-zero emissions targets set by the Intergovernmental Panel on Climate Change (IPCC). Given the urgency to reduce greenhouse gas emissions, traditional methods that estimate SOC over 100 years must be revised. Hence, a novel fate transport numerical model was developed to forecast SOC levels relevant to individual countries' net-zero targets in various time frames. The simulation results revealed that most countries had sufficient organic amendment to mitigate the CO2 emission of that country for a year if the organic amendment was applied on 20% of the arable land. However, if a significant fraction of the total CO2 emissions needs to be mitigated before reaching the net zero target, the requirements of organic amendments need to be increased several folds. All the available agricultural land should also be brought under the organic amendment regime. Later, the dynamic LCA approach was undertaken for estimating Global Warming (GWP) from land-applied organic residue. It was observed that, depending on the dynamic LCA model, the estimated GWP was different. However, the estimated dynamic GWP was very close to the residual SOC calculated through the fate transport model. The mass of organic residues generated from a biorefinery was examined by employing a waste biorefinery model to explore further the routes of acquiring additional organic amendment. Simulated results showed that while a waste biorefinery could not provide additional organic residue compared to the original organic waste input, it was highly efficient for nutrient recovery and its uses. This study demonstrated that organic amendment-based carbon sequestration adequately mitigated residual GHG at the net-zero target. [ABSTRACT FROM AUTHOR]

Agricultural land parcels (ALPs) are essential for effective agricultural management, influencing activities ranging from crop yield estimation to policy development. However, traditional methods of ALP delineation are often labor-intensive and require frequent updates due to the dynamic nature of agricultural practices. Additionally, the significant variations across different regions and the seasonality of agriculture pose challenges to the automatic generation of accurate and timely ALP labels for extensive areas. This study introduces the cadastral-to-agricultural (Cad2Ag) framework, a novel approach that utilizes cadastral data as training labels to train deep learning models for the delineation of ALPs. Cadastral parcels, which are relatively widely available and stable elements in land management, serve as proxies for ALP delineation. Employing an adapted U-Net model, the framework automates the segmentation process using remote sensing images and geographic information system (GIS) data. This research evaluates the effectiveness of the proposed Cad2Ag framework in two U.S. regions—Indiana and California—characterized by diverse agricultural conditions. Through rigorous evaluation across multiple scenarios, the study explores diverse scenarios to enhance the accuracy and efficiency of ALP delineation. Notably, the framework demonstrates effective ALP delineation across different geographic contexts through transfer learning when supplemented with a small set of clean labels, achieving an F 1 -score of 0.80 and an Intersection over Union (IoU) of 0.67 using only 200 clean label samples. The Cad2Ag framework's ability to leverage automatically generated, extensive, free training labels presents a promising solution for efficient ALP delineation, thereby facilitating effective management of agricultural land. [ABSTRACT FROM AUTHOR]

Beef and dairy cattle commonly ingest soil when consuming forage-based diets in paddock feeding situations. However, the extent of this soil intake is poorly understood in the Australian environment and under Australian grazing systems. Therefore, the aim of the current literature review was to examine soil ingestion in cattle and the factors that affect ingestion. We found 11 studies containing soil-ingestion data, based in England, France, New Zealand and USA but none from Australia. A wide range in soil ingestion rates was reported and intake varied considerably with season, forage-pasture type, pasture-on-offer, stocking rate and grazing conditions. Generally, soil ingestion was lower in beef cattle than in dairy cattle. We considered the differences in reported levels, variables around those differences, and reliability of methodology used, and compared results with existing international guidelines. For Australian applications, we deduced and recommended that 0.5 kg/head.day is used until field-based research is conducted in Australia which might provide a more specific value for Australian conditions. Our review will inform future livestock management, particularly on contaminated agricultural land. In Australia, there is a poor understanding of how much soil cattle ingest while grazing. We conducted a literature review and found 11 papers to inform our views, but there were no published papers from Australia. Ingestion levels varied among countries, and between dairy and beef enterprises. We estimated that cattle ingest about 0.5 kg/head.day; however, this varies according to circumstance. Research is required in Australian environment to better inform managers. [ABSTRACT FROM AUTHOR]

Billions of genomic sequences and records of species occurrence are available in public repositories (e.g. National Center for Biotechnology Information, NCBI and the Global Biodiversity Information Facility, GBIF). By implementing analytical tools from different scientific disciplines, data mining these databases can aid in the global surveillance of zoonotic pathogens that circulate among wildlife. We illustrate this by investigating the Hantavirus–rodent system in the Americas, i.e. New World Hantaviruses (NWH). First, we considered the circulation of pathogenic NWH among Cricetidae rodents, by inferring the phylogenetic links among 277 genomic samples of the S segment (N protein) of NWH found in 55 species. Second, we used machine learning to assess the impact of land use on the probability of presence of the rodent species linked with reservoirs of pathogenic Hantaviruses. Our results show that hosts are widely present across the Americas. Some hosts are present in the primary forest and agricultural land, but not in the secondary forest, whereas other hosts are present in secondary forest and agricultural land. The diversity of host species allows Hantavirus to circulate in a wide spectrum of habitats, in particular rural rather than urban. We highlight that public repositories of genomic data and species occurrence are very useful resources for monitoring potential enzootic transmission and spillover of zoonotic viruses in relation with the changes that humans produce in the biosphere. [ABSTRACT FROM AUTHOR]

Spatially explicit models are an important component of systematic conservation planning, enabling the depiction of biodiversity metrics across landscapes and objective evaluation of candidate sites for conservation delivery. However, sites considered "best" for conservation are typically viewed from the standpoint of biological value and may not be the most effective or efficient when risk of habitat loss, cost of conservation, intended conservation treatments, and overall conservation strategy are considered. We evaluated risk of habitat loss, land cost, and landscape context for geographic areas harboring most‐dense to least‐dense population quartiles for 16 species of grassland birds in the US northern Great Plains. Differences in land cost, risk of grassland conversion, and landscape context among quartiles and species indicated that a minimum‐area strategy may be inefficient and even ineffective. Priority zones for western species were generally associated with lower agricultural land cost, more protected land, and landscape characteristics associated with intact grasslands; eastern species were generally associated with higher agricultural land cost, tillage probability, grass loss, cropland, development, forest, Conservation Reserve Program grasslands, and distance to grass. Our results indicate that addressing areas outside of population cores increases conservation options and may provide substantial benefits to portions of populations that are most vulnerable to habitat loss or other stressors. [ABSTRACT FROM AUTHOR]

Past land-use reconstructions are a key tool for studying long-term human ecodynamics and addressing pressing questions about the origins and evolutionary dynamics of the Anthropocene. In particular, agricultural landcover reconstructions are vital for understanding long-term human-environment dynamics. Most past agricultural land-use models, however, rely heavily on modelling assumptions, make limited use of known archaeological site locations to constrain or inform their estimates and tend to be limited to general estimates of percentages of plant types within catchments around pollen trapping lakes. The lack of information outside catchment areas and low spatial resolution even within catchments constrain the utility of these models. To address this problem, we propose a new approach that combines archaeological predictive modelling with pollen-based agricultural landcover reconstructions to produce more accurate, spatially explicit past landcover estimates. Here, we present the results of a case study deploying the new approach to produce improved past landcover maps for a region in the Western Taurus Mountains, southwestern Turkey. The study area surrounds Sagalassos, an antique urban centre with a regional settlement history encompassing nine millennia. We produced five archaeological predictive models for the study region using the 'Locally Adaptive Models of Archaeological Potential' (LAMAP) method spanning the Hellenistic through Late Ottoman period. We then combined those predictive surfaces with 'Regional Estimates of VEgetation Abundance from Large Sites' (REVEALS) pollen landcover reconstructions for the same periods based on pollen from sediment cores extracted from three catchments within the study area. Lastly, we compared the resulting hybrid landcover models to the archaeological record using data not used to make the predictions. We found that the hybrid landcover model aligned very well with the known extents of agricultural land-use from the study area. These results indicate that the proposed approach is a viable way to combine pollen-based landcover models with archaeological data and produce more accurate, empirically-based landcover reconstructions reflecting real human activity in the past. [ABSTRACT FROM AUTHOR]

In recent years, overseas labour migration has become a lifeline for many households in Nepal. Using survey data from 465 randomly selected households and 227 overseas labour migrants, this paper examines the factors influencing overseas labour migration and migration intensity in farming households by employing a generalised two-part fractional regression model, and migrants' destination choice by using the probit model. We find that a higher proportion of educated members in the household, along with household's credit access, indebtedness, and contacts with manpower agencies significantly increase the likelihood of overseas labour migration and migration intensity in the households, whereas the presence of employed members in the household, larger farms, irrigation access, and higher asset index significantly lower such likelihood. We find a lower likelihood of migration to Malaysia and the Middle East countries among individuals with employed household members and a higher asset index, while those connected to manpower agencies are more likely to choose these countries as destinations. Our findings emphasise the significance of creating and providing decent economic opportunities, including strengthening the agriculture sector, to address the existing surge in overseas labour migration from farming households in Nepal. [ABSTRACT FROM AUTHOR]

The expansion of sugarcane, a tropical high‐yielding feedstock, will likely reshape the Southeastern United States (SE US) bioenergy landscape. However, the sustainability of sugarcane, particularly as it displaces grazed pastures, is highly uncertain. Here, we investigated how pasture conversion to sugarcane in subtropical Florida impacts net ecosystem CO2 exchange (NEE) and net ecosystem carbon (C) balance (NECB). Measurements were made over three full growth cycles (> 3 years) in sugarcane—plant cane, PC; first ratoon cane, FRC; second ratoon cane, SRC—and in improved (IM) and semi‐native (SN) pastures, which make up ca. 37% of agricultural land in the region. Immediately following conversion, PC was a stronger net source of CO2 than pastures, indicating the importance of CO2 losses related to land disturbance. Sugarcane, however, shifted to a strong net sink of CO2 after first regrowth, and overall sugarcane was a stronger net CO2 sink than pastures. Both stand age and low water availability during cane emergence and tillering substantially decreased its potential gross CO2 uptake. Accounting for all C gains and removals (i.e., NECB), greater frequency of burn events and repeated harvest increased removals and overall made sugarcane a stronger C source relative to pastures despite substantial C inputs from the previous land use and a stronger CO2 sink strength. Time since conversion substantially reduced C losses from sugarcane, and the NECB of SRC was similar to that of IM pasture but lower than that of SN pasture, indicating a rapid shift in the NECB of cane. We conclude that the C‐balance implications following conversion will depend on the proportion of IM versus SN pastures converted to sugarcane. Furthermore, our findings suggest that no‐burn harvest management strategies will be critical to the development of a sustainable bioenergy landscape in SE US. [ABSTRACT FROM AUTHOR]

In recent years, urban flooding has emerged as a major challenge, with land cover change identified as a key contributing factor. This study investigates the sensitivity of urban flooding risk to land cover changes. Seven urban land cover maps from different years and five different rainfall events, were selected as the examples. Based on hydrodynamic model simulations, this study analyzed the relationship between the total area of urban flooding and the proportion of ponding depths across various depth intervals and the land cover change. The study region was divided into 41 sub-areas based on road classifications and building clusters. The urban flood risk considering the aggregation of urban flooding, maximum ponding depth, the extent of the ponded area, and the average ponding depth was quantified within these sub-regions. Additionally, ten characteristic points were extracted from two sub-area with significant risk changes to analyze the logic of urban flooding risk evolution under land use change. The results indicate that: (1) There is a positive correlation between the total area of urban flooding and the proportion of high ponding depths and increasing impervious surfaces. (2) Urbanization significantly increases urban flooding risk, with 28 out of 41 areas experiencing heightened risk, including 6 sub-areas with risk increases exceeding 100%. (3) When the rainfall event changes from a 20-year to a 100-year return period, the maximum ponding depth in cropland stabilizes compared to impervious surfaces. Conversion of cropland to impervious surfaces accelerates increases in ponding depth and can lead to higher maximum ponding depths. [ABSTRACT FROM AUTHOR]

Fossil fuels, particularly oil, face sustainability challenges due to depletion and their role in increasing atmospheric CO2 levels, contributing to climate change and impacting global agriculture. Renewable energy sources like solar power offer a viable alternative. This study explores the feasibility of agro-photovoltaic (APV) systems, which integrate solar panels with agricultural land to generate electricity while cultivating crops. Specifically, the impact of APV systems on rice production and quality was investigated. Solar modules with a total capacity of 99.84 kW were installed on a 2580 m² site, with two module configurations tested. Two rice (Oryza sativa L.) cultivars, Woonkwang and Saenuri, were transplanted and monitored for growth characteristics, chlorophyll content, and fluorescence, as well as yield and quality. Growth analysis of rice under APV systems showed minimal impact on plant height and tiller numbers, though chlorophyll content analysis indicated delayed leaf aging and extended maturation time. Rice yield decreased due to altered panicle and spikelet numbers. Quality analysis revealed changes in head rice rate and broken rice, stressing optimal harvest timing in APV systems. Shading conditions also affected physicochemical properties and taste profiles. Yields dropped by about 20% under APV systems, but the financial returns from electricity generation significantly outweighed the crop revenue loss. Despite the high initial installation costs and regulatory challenges, APV systems present a promising dual-use approach for enhancing farm income and promoting renewable energy. Continued research and investment are essential for optimizing APV systems and expanding their adoption. [ABSTRACT FROM AUTHOR]

Soil suppressiveness can reduce the damage by plant parasitic nematodes (PPN) in agricultural soils and is conveyed by the activity of soil microorganisms. While natural suppressiveness has been reported, it is still poorly understood if soil suppressiveness can be elicited by manipulating the soil microbial community. In the present study we assessed the number of the Pratylenchus penetrans (Pp) and the bacterial and fungal community composition over 7 years in a long-term soil health experiment. The field experiment consisted of an organic and conventional agricultural land management system and three soil health treatments (SHT): an untreated control (CT), anaerobic disinfestation (AD) and a combination of marigold cover cropping, compost and chitin amendment (CB). The land management systems were kept continuously, while the soil health treatments were applied only twice in seven years. The microbial community significantly differed between the organic and conventional system, but there was no significant difference in Pp numbers between the two systems. However, both the CB treatment and to a lesser extent the AD treatment reduced Pp numbers and increased yield with the effect being the strongest in the years immediately after the treatment. Accordingly, both the bacterial and fungal community differed significantly between the treatments, the differences being largest in the years after the treatments. Notably, the CB treatment elicited both long-term changes in the microbial community and a reduction of Pp numbers lasting for at least three years. These results indicated that a combination of treatments can lead to an altered soil microbial community in combination with persisting suppressiveness of Pp. [ABSTRACT FROM AUTHOR]

Cropland (CRL) abandonment is a worldwide phenomenon of land use change with significant impacts on agro‐ecosystems. This research attempts to deepen the analysis of the positive and negative effects arising by focusing on the changes in soil properties and the amount and composition of soil particles detached after several periods of rainfall and the resulting exported sediment that occurs in abandoned areas with natural revegetation compared to CRL. The study was carried out in an agroforestry catchment with a temperate climate, on which CRL and abandoned land with natural vegetation were compared. The soil was sampled along two representative hillslopes integrating elements of the landscape and land use/land covers. Sediments were collected after seven periods in which flood events were recorded during the period July 2016 to December 2017, using artificial‐lawn mats. Soil and sediment composition (texture, soil organic carbon [SOC] and nutrients [total nitrogen and total phosphorous]) under both land uses were assessed and compared and related to rainfall characteristics using principal component analysis. Nutrient enrichment factors in the sediments compared to soils were also evaluated. The results highlight that after abandonment, SOC increased significantly, reaching contents almost three times higher than in CRL. Consequently, soil erodibility decreased, resulting in substantially lower sediment generation after erosive rainfall events. On average, sediment generation was three times lower in abandoned areas than in CRL, despite their steeper slopes. Soil total nitrogen also increased on abandoned lands, reaching values about twice as high as those in CRL. However, total phosphorous content was almost twice as high in CRL than in abandoned land posing a potential risk for water due to higher erosion rates recorded in CRL. The results confirmed the association of phosphorous with smaller particles and also demonstrated the total phosphorous‐SOC link in abandoned land. Furthermore, the effect of rainfall intensity on phosphorous mobilisation was confirmed, whilst nitrogen losses were mainly related to the total amount of rainfall recorded. In a scenario of increasing extreme precipitation, both total amount and increased precipitation intensity may exacerbate water pollution problems following nutrient loss in cultivated areas. This research contributes to identifying the impacts on agroforestry systems within the current context of converting natural areas into cultivated land, whilst in other regions revegetated natural areas are developed from abandoned agricultural land. [ABSTRACT FROM AUTHOR]

A major global catastrophe would likely disrupt trade in liquid fuels. Countries dependent on imported oil products might struggle to sustain industrial agriculture. Island nations importing 100% of refined fuels are particularly vulnerable. Our case study aimed to estimate the agricultural land area and biofuel volumes needed to feed the population of New Zealand in the absence of trade. Results showed that stored diesel would quickly be exhausted with ordinary use (weeks) and even with strict rationing (months). To preserve fuel, we found that farming wheat (requiring as little as 5.4 million liters [L] of diesel per annum) was more fuel‐efficient than potatoes (12.3) or dairy (38.7) to feed the national population under a climate‐as‐usual scenario. In a nuclear winter scenario, with reduced agricultural yields, proportionately greater diesel is needed. The wheat would require 24% of current grain‐cropped land, and the canola crop used as feedstock for the required biofuel would occupy a further 1%–7%. Investment in canola biodiesel or renewable diesel refineries could ensure supply for the bare minimum agricultural liquid fuel needs. Were subsequent analysis to favor this option as part of a fuels resilience response and as a tradeoff for routine food use, expansion in refining and canola cropping before a catastrophe could be encouraged through market mechanisms, direct government investment, or a combination of these. Logistics of biofuel refining scale‐up, post‐catastrophe, should also be analyzed. Further, biodiesel produced in normal times would help the nation meet its emissions reduction targets. Other countries should conduct similar analyses. [ABSTRACT FROM AUTHOR]

The dwindling availability of agricultural land, caused by factors such as rapid population growth, urban expansion, and soil contamination, has significantly increased the pressure on food production. To address this challenge, cultivating non-food crops on contaminated land has emerged as a promising solution. This approach not only frees up fertile soil for food production but also mitigates human exposure to contaminants. This work aimed to examine the impact of soil contamination with Cd, Pb, Ni, and Zn on the growth, productivity, metal accumulation, and the tolerance of five lignocellulosic non-food crops: switchgrass (Panicum virgatum L.), biomass sorghum (Sorghum bicolor L. Moench), giant reed (Arundo donax L.), African fodder cane (Saccharum spontaneum L. spp. aegyptiacum Willd. Hackel), and miscanthus (Miscanthus × giganteus Greef et Deu.). A two-year pot experiment was conducted in Greece, Italy, and Portugal, following the same protocols and applying various levels of metals: Cd (0, 4, 8 mg kg−1), Pb and Zn (0, 450, 900 mg kg−1), and Ni (0, 110, 220 mg kg−1). The experimental design was completely randomized, with three replicates for each treatment. The results showed that switchgrass and sorghum generally maintained their height and productivity under Cd and Pb stress but were adversely affected by high Zn and Ni concentrations. Giant reed and African fodder cane showed reduced height and productivity at higher Ni and Zn levels. Miscanthus exhibited resilience in height but experienced productivity reductions only at the highest Zn concentration. Heavy metal uptake varied among crops, with switchgrass and sorghum showing high Cd and Pb uptake, while giant reed accumulated the most Cd and Zn. Miscanthus had the highest Ni accumulation. The tolerance indices indicated that switchgrass and sorghum were more tolerant to Cd and Zn at lower concentrations, whereas miscanthus had lower tolerance to Cd but a higher tolerance to Zn at higher concentrations. Giant reed and African fodder cane demonstrated stable tolerance across most heavy metals. Accumulation indices highlighted the effectiveness of switchgrass and sorghum in Cd and Pb uptake, while miscanthus excelled in Ni and Zn accumulation. The cluster analysis revealed similar responses to heavy metal stress between African fodder cane and giant reed, as well as between sorghum and miscanthus, with switchgrass displaying distinct behavior. Overall, the study highlights the differential tolerance and accumulation capacities of these crops, indicating the potential for phytoremediation applications and biomass production in heavy metal-contaminated soils. [ABSTRACT FROM AUTHOR]

The need to control soil erosion has received increasing attention, but quantitative data on the sources of suspended sediment in many river–reservoir systems is still lacking. The goal of this research was to compare the application of spectroscopic [mid‐infrared (MIR)] and mineralogical [X‐ray diffraction (XRD)] fingerprints for assessing relative sediment source contributions from different land use groups (agricultural lands, forests and human settlements) in the Konar–Damodar river–reservoir system in India. Source apportionment was estimated using partial least square (PLS) regression for spectroscopic tracers (MIR) and the Bayesian MixSIAR model for mineralogical tracers. Both methods identified differences between the pre‐ and post‐monsoon sediment contributions of forests (overall contribution bounds of ~35–43%). During monsoon seasons, both fingerprinting methods indicated agricultural land use as the primary source of suspended sediment. Although there were some temporal variations in the predicted contributions of the land use sources, the MIR‐PLS and mineralogical–MixSIAR methods produced comparable ranges. The respective variations in contributions, using MIR‐PLS and mineralogical–MixSIAR, were ~31 to 66% compared with ~36 to 61% for agricultural lands, ~21 to 43% compared with ~15 to 39% for forests and ~16 to 37% compared with ~19 to 32% for human settlements. [ABSTRACT FROM AUTHOR]

Estimating gross primary production (GPP) of terrestrial ecosystems is important for understanding the terrestrial carbon cycle. However, existed nationwide GPP datasets are primarily driven by coarse spatial resolutions (≥500 m) remotely sensed data, which fails to capture the spatial heterogeneity of GPP across different ecosystem types at land surface. This paper introduces a new GPP dataset, Hi-GLASS GPP v1, with a fine spatial resolution (30-m) and monthly temporal resolution from 2016 to 2020 in China. The Hi-GLASS GPP v1 dataset is generated from 30-m Landsat data using a process based light use efficiency model. The Hi-GLASS GPP v1 model integrates a detailed map of maize plantations, a crucial C4 crop in China known for its higher photosynthetic efficiency compared to C3 crops. This inclusion helps correct the underestimation of GPP that typically occurs when all croplands are categorized as C3. The Hi-GLASS GPP v1 dataset demonstrates a robust correlation with GPP data derived from eddy covariance towers, thereby enabling a more accurate assessment of terrestrial carbon sequestration across China. [ABSTRACT FROM AUTHOR]

Justice in sustainability transitions requires states to design transition policies that 'leave no‐one behind'. Emphasising fairness, however, may entail slowing or scaling down the impetus of sustainability transition. To examine this risk empirically, we analysed how stakeholders frame justice in deliberating policy measures needed to support just transition in agricultural land use and dietary changes, the cornerstones of building healthier and climate friendlier food systems in Finland. The results show that justice frames focus on the potential impacts of transition, largely ignore global scale, and prioritise social justice claims at the cost of environmental ambition. To create just and environmentally effective policies, policymakers need to consider justice at the level of policy‐mixes combining environmental and social policies. Furthermore, they need to acknowledge systemic injustices present in existing systems while striving towards just transition. [ABSTRACT FROM AUTHOR]

Food waste in Australia is causing a massive financial burden on the country, with an estimated annual cost of $36.6 billion. Several factors, such as food loss and waste (FLW), inflation, unemployment, and low income, concurrently contribute to Australia's food insecurity problem. This study mainly focuses on FLW, which plays a significant role in Australia's food insecurity. This study finds that despite taking numerous initiatives, including the National Food Waste Strategy 2017, National Food Waste Baseline 2021, Australian Food Pact, and Sector Action Plan, the FLW approximately 7.9 million tonnes of food is wasted every year in Australia. This study explores that in 2022, food insecurity severely affects 21% or 2.1 million households, and 94% of households are concerned about not having enough food in Australia. The author finds that the FLW contributes to producing 17.5 m tonnes of carbon dioxide (CO2), 2628.3 gigaliters of water use, and 25 m + hectares of agricultural land use by generating food production. Therefore, this study aims to critically evaluate the FLW scenario and management strategies of the Australian government in order to achieve the sustainable development goal‐12 (responsible consumption and production) and target 12.3 of reducing food waste by 50% by the year 2030. [ABSTRACT FROM AUTHOR]

This paper highlights the significance of nurturing and safeguarding minor crops as a means of achieving regional sustainable development, preserving biodiversity, and ensuring food security. Minor crops play a critical role in addressing various environmental challenges like climate change, biodiversity loss, desertification, and soil salinity. Simultaneously, they offer local farmers economic opportunities by providing income stability and resilience. To demonstrate the potential of minor crops in promoting sustainable development, a case study from Greece is presented, focusing on three crops: blackcurrant, rice, and sunflower, which bring substantial economic and environmental benefits to the region. The study assesses the economic and environmental impact of these crops across three different rural territories in Greece. Despite covering less than 4% of total agricultural land, these crops are predominantly grown in marginal, mountainous, and insular regions. By analyzing the farm‐level and broader economic effects, the study determines their potential for agricultural and regional development. The results indicate that these crops are more profitable and environmentally friendly compared to their counterparts in all three cases. Additionally, they make significant contributions to the regional economies relative to their cultivated land area, supporting economic growth while conserving and enhancing agricultural land. In conclusion, this article emphasizes the crucial need to promote the cultivation and preservation of minor crops to support sustainable development, biodiversity conservation, and regional economic growth. Policymakers and agricultural practitioners should prioritize the cultivation and preservation of regional minor crops to safeguard biodiversity at the regional level and at the same time sustainable regional development. [ABSTRACT FROM AUTHOR]

Atrazine, a herbicide used for controlling broadleaf weeds, has been one of the predominant pollutants constituting 80–90% of detection frequency in the samples collected from rivers, estuaries, oceans, sediments, agricultural lands, and crops. The fate of atrazine is highly unpredictable depending on the physio-chemical, physiological and geographical conditions. Range of metabolites such as deethylatrazine (DEA), deisopropyl atrazine (DIA), and didealkylatrzine (DDA) are formed as a result of biotic as well as abiotic degradation process in the environment following cyanuric acid, ammelide, CO2 and NH3 are formed as final products. Atrazine degraded products has shown more hazardous nature than the parent compound, atrazine. Atrazine is banned in Italy, India, Germany and European union but widely used in China, Australian, Canadian and US agriculture. To date, reviews evaluating the assimilation of synerigistic treatment technologies and comparative degration mechanism have not been highlighted. This work focuses on (1) the spatiotemporal distribution of atrazine and its metabolites globally and the factors governing it (2) provides an in-depth discussion about the various studies showing the toxicity of atrazine in microbes, cattle, human, terrestrial and aquatic organisms; (3) discusses the contaminants of emerging concern which are continuously replacing atrazine like terbuthylazine and their intermediate compounds posing more risk to wildlife and humans; (4) summarises the different treatment technologies which have been predominantly applied for the removal of atrazine in water and soil systems and also discusses the synergistic or mutualistic aspects of treatment methods in degrading atrazine. [ABSTRACT FROM AUTHOR]

Background: Agricultural irrigation is an important practice to safeguard crops against drought and enhance grain yield in arid regions. The Hexi Corridor, known as a classic arid region, faces significant pressure on agricultural production and food security due to the scarcity of water resources. There is an urgent need to investigate agricultural water use of the irrigated regions. Water use efficiency (WUE), defined as the ratio of gross primary productivity (GPP) to actual evapotranspiration (ET), serves as a valuable indicator linking carbon assimilation and water loss. It enables the quantification of areas where water can be utilized more effectively. However, the long-term spatiotemporal dynamics of WUE and driving mechanism in the irrigated areas of the Hexi Corridor remain unclear. Results: This study used GPP calculated by a light use efficiency model (EF-LUE), ET estimated by an ETMonitor model and irrigated cropland maps across China (IrriMap_CN) to examine the spatiotemporal dynamics of irrigated cropland WUE and its controlling factors in the Hexi Corridor from 2001 to 2018. The results are as following: (1) The average annual WUE was approximately 1.34 ± 0.38 g C kg−1 H2O yr−1, with an increasing trend of 0.012 g C kg−1 H2O yr−1, and faster growth observed during 2011–2018 compared to 2001–2010. (2) The contribution of GPP to WUE trends and WUE interannual variability (IAV) was greater than that of ET. (3) The dominant climatic factors of WUE IAV in the Hexi Corridor were SPEI, precipitation, and soil moisture. (4) The standardized Structural Equation Model (SEM), incorporating the relationship between WUE and factors such as water, energy, NDVI, and water-saving irrigation, explained 81% of the variation in irrigated cropland WUE. Here, biological factors (GPP and NDVI) were the primary factors influencing WUE variability, and water-saving irrigation had a stronger indirect effect than climate factors (water and energy) on variation in WUE. Conclusions: Our findings offer valuable theoretical insights into the mechanisms governing the interaction between the carbon and water of irrigated cropland, guiding the management of water resources and land in agricultural practices within the Hexi Corridor. [ABSTRACT FROM AUTHOR]

Pesticides in surface freshwater primarily originate from their emissions in agricultural lands, potentially leading to violations of surface freshwater quality standards. To aid global regulatory agencies in effectively managing surface freshwater quality by estimating and controlling pesticide emission rates, this study proposes a hierarchical screening approach for countries and regions worldwide to select appropriate modeling tools. Hierarchical indicators are introduced to classify countries globally, considering their spatial distribution areas, pesticide emission conditions, and legislative systems. Consequently, different categories of countries are matched with suitable model groups, such as the standard model group for regulatory scenarios, the general model group for continental scenarios, and the advanced model group with high spatial resolution. Results indicated that a total of 193 countries worldwide were categorized into six country groups, of which 153, 34, and 6 countries were found to fit the standard, general, and advanced model groups, respectively, based on the model assignments for these country groups. Furthermore, 12 commonly used pesticides were selected to demonstrate the back-calculation process, which estimates the pesticide emission rate (input) by pesticide surface freshwater quality standards (output) by standard and general model groups. The Advanced model group was not applied in this process due to its intensive computation. An approximate approach was developed to simplify the calculation of the emission rate factor of pesticides using the PWC and TOXSWA selected in the standard model group as well as SWAT in the general model group, serving as a demonstration. This approach can be applied to control pesticide emission rates from surface freshwater quality standards across countries that fit in the standard and general model groups. The results highlight that pesticide fate models selected through the hierarchical screening approach, can assist global countries in establishing a quantitative relationship between pesticide emission rates and surface freshwater quality standards, which can help global agencies manage pesticide emissions and freshwater quality from a legal perspective. There is a need to update and simplify suitable advanced model for calculation demonstration in future studies to aid in pesticide management. Further research is needed to thoroughly investigate pesticide emissions and freshwater residue concentrations under varying conditions. [ABSTRACT FROM AUTHOR]

Nature-based solutions are approaches that draw inspiration from and are supported by nature, offering environmental, social, and economic benefits for enhancing urban resilience and sustainability. One of the greatest land use changes affecting the urban landscape is agricultural land conversion which affects the food security. Urban agriculture will not only generate livelihood opportunity for the urban poor but be the urban lung and heat sink, allow solid and water waste disposal and facilitate eco-cultural community bonding. A vast majority of the global urban dwellers live in the small and medium sized cities. However very little study has been done on urban agriculture in these areas although agricultural loss is more pronounced here. The objective of this paper is to examine through remote sensing data, in a time series between 2011 to 2023, the loss in agricultural land in one such smaller city- Bhubaneswar, which lies on a very fertile agricultural belt. This paper examines the decline of agricultural land in comparison to the rise in built up area within the specified timeframe and goes on to suggest a nature-based solution which makes the city self-sufficient in terms of food security. The research methodology involves mapping the spatial distribution of both the supply and demand for ecosystem services and quantifying the impact of Agriculture land use conversion on agriculture production. It concludes by deriving a framework which will identify the policy and governance interventions required so that the SDG goals 2 and 11.3 are realized. [ABSTRACT FROM AUTHOR]

Background: During the fall of 2020, the porcine reproductive and respiratory syndrome virus (PRRSV) L1C.5 variant emerged and rapidly spread throughout southern Minnesota generating questions regarding possible transmission routes. This study aimed to investigate whether PRRSV could be detected on surfaces inside and outside pig barns housing L1C.5 variant PRRSV-positive pigs to illustrate the potential for indirect transmission of PRRSV. Seven Midwestern U.S. PPRS-positive breeding or growing pig farms and one PRRS-negative farm were conveniently selected. Internal and external barn surfaces were wiped using a PBS moistened cloth and the resulting liquid was submitted to the University of Minnesota Veterinary Diagnostic Laboratory for PRRSV RT-PCR testing and virus isolation. Results: All (n = 26) samples from PRRSV-negative farm tested negative. Nineteen (13%) out of 143 samples from positive farms yielded positive RT-PCR results. Positive samples originated primarily from exhaust fan cones and doorknobs, followed by anteroom floor and mortality carts/sleds. Virus isolation attempted on two samples did not yield positive results. Conclusions: PRRSV contamination can occur on surfaces inside and outside pig barns that are in frequent contact with farm personnel. Although virus isolation attempts were negative, our results illustrate the potential for PRRSV to be transmitted indirectly through contaminated materials or farm personnel. The study supports the implementation of biosecurity practices by farm personnel to prevent the introduction of PRRSV into farms and the prevention of PRRSV transmission between farms. [ABSTRACT FROM AUTHOR]

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Visible and near-infrared (Vis-NIR) reflectance spectroscopy has recently emerged as an efficient and cost-effective tool for monitoring soil parameters and provides an extensive array of measurements swiftly. This study sought to predict fundamental biological attributes of calcareous soils using spectral reflectance data in the Vis-NIR range through the application of partial least square regression (PLSR) and stepwise multiple linear regression (SMLR) techniques. The objective was to derive spectrotransfer functions (STFs) to predict selected soil biological attributes. A total of 97 composite samples were collected from three distinct agricultural land uses, i.e., sugarcane, wheat, and date palm, in the Khuzestan Province, Iran. The samples were analyzed using both standard laboratory analysis and proximal sensing approach within the Vis-NIR range (400–2500 nm). Biological status was evaluated by determining soil enzyme activities linked to nutrient cycling including acid phosphatase (ACP), alkaline phosphatase (ALP), dehydrogenase (DEH), soil microbial respiration (SMR), microbial biomass phosphorus (Pmic), and microbial biomass carbon (Cmic). The results indicated that the developed PLSR models exhibited superior predictive performance in most biological parameters compared to the STFs, although the differences were not significant. Specifically, the STFs acceptably accurately predicted ACP, ALP, DEH, SMR, Pmic, and Cmic with R2val (val = validation dataset) values of 0.68, 0.67, 0.65, 0.65, 0.76, and 0.72, respectively. These findings confirm the potential of Vis-NIR spectroscopy and the effectiveness of the associated STFs as a rapid and reliable technique for assessing biological soil quality. Overall, in the context of predicting soil properties using spectroscopy-based approaches, emphasis must be placed on developing straightforward, easily deployable, and pragmatic STFs. [ABSTRACT FROM AUTHOR]

The direction and magnitude of soil organic carbon stock (SOCS) change following land use and land cover change (LULCC) are highly uncertain, largely due to the lack of relevant global soil data. Great efforts have been made to build SOCS database at regional, national and even sub-continental scales following LULCC; however, a comprehensive and open-access global database has not yet been developed, hindering a deep understanding of LULCC impact on SOCS dynamics. In this study, we introduce a new global SOCS database for LULCC, compiled from 639 articles documented in the Web of Science through the end of 2023. Targeting five major land uses (cropland, grasslands, forest, plantation, and savanna), this database – named the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change (GSOCS-LULCC) – include 1,206 sites with 5,982 records at various sampling depths. The database will enable users to assess the global impact of LULCC on SOCS dynamics and identify the factors that control SOCS changes for specific types of LULCC. The GSOCS-LULCC database is freely available from the Zenodo platform at https://doi.org/10.5281/zenodo.11183819 (Chen et al., 2024). [ABSTRACT FROM AUTHOR]

Understanding the impact of herbivores on croplands is critical to developing sustainable agricultural landscapes. Here, we test the hypothesis that field size and landscape structure modulate the levels of herbivory in cowpea and maize subsistence croplands in Brazilian drylands. We conducted the study in nine small-scale farms with varying levels of landscape structure and field sizes (0.10–1.67 ha). In each farm, we haphazardly sampled 20 plants of maize and cowpea. The frequency of foliar herbivory was high for both crops (0.45–0.72), but the average magnitude of damage was low (<3% of leaf surface in both crops), indicating that herbivory is not an ecosystem disservice. Nonetheless, the field size and level of human impact on the landscape matrix were positively related to the frequency and magnitude of damage on maize foliage and cowpea foliage and pods. Landscape composition (proportion of woodland/forest surrounding the field) and configuration (number of surrounding woodland/forest patches) had weaker effects on herbivory levels, mixing positive and negative effects on both crops. Our findings indicate that herbivory attack is not concerning in the agricultural fields studied, but suggest that maize and cowpea should be preferably grown in small fields surrounded by an environmental-friendly matrix and patches of conserved ecosystems. [ABSTRACT FROM AUTHOR]

Greenhouse internal microclimate has been proven to be non-homogeneous in many aspects. However, this variability is only sometimes considered by greenhouse models, which often calculate climatic variables without any spatial reference. Farmers, on the other hand, may wish to have these differences highlighted as they could lead to aimed actions only for a specific area of the greenhouse, while at the same time, they are not willing to invest in sensors to be installed everywhere. This paper presents a data-driven methodology to generate a virtual 2D map of a greenhouse, which allows farmers to control any critical parameter they desire with minimum investment, as monitoring is done via soft sensing with only a few actual sensors. The proposed flow starts with a set of temporary sensors placed in the points of interest; then, a model for each of them is developed via linear regression and, finally, a map of the entire area can be derived by interpolating values from these models. This allows the generation of accurate models at a reduced cost as temporary sensors can be reused at other locations. The methodology has been tested on adjacent greenhouses and in two farms, where temperature and other climatic variables have been monitored. Experimental results show that the proposed methodology can reach an adjusted R2 value of 98% for predicting values in different greenhouse locations. [ABSTRACT FROM AUTHOR]

New particle formation (NPF) is the atmospheric process whereby gas molecules react and nucleate to form detectable particles. NPF has a strong impact on Earth's radiative balance as it produces roughly half of global cloud condensation nuclei. However, time resolution and sensitivity of current instrumentation are inadequate in measuring the size distribution of sub‑3 nm particles, the particles critical for understanding NPF. Here we present the Condensation Particle Counters For Atmospheric Rapid Measurements (CPC FARM), a method to measure the concentrations of freshly nucleated particles. The CPC FARM consists of five CPCs operating in parallel, each configured to operate at different detectable particle sizes between 1–3 nm. This study explores two methods to calculate the size distribution from the differential measurements across the CPC channels. The performance of both inversion methods were tested against the size distribution measured by a pair of stepping particle mobility sizers (SMPS) during an ambient air sampling study in Pittsburgh, PA. Observational results indicate that the CPC FARM is more accurate with higher time resolution and sensitivity in the sub-3 nm range compared to the SMPS. [ABSTRACT FROM AUTHOR]

Land abandonment promotes forest expansion into abandoned agricultural lands in Europe. This process leads to changes that affect several ecosystem services, but it is considered a low‐cost strategy to restore soil functions in past agricultural lands. Soil microbial communities play a key role in restoring functions. However, the relationship among forest expansion, microbial communities, and soil functioning is unclear. In this study, we used a Juniperus thurifera expansion gradient on abandoned agricultural lands in Alto Tajo National Park (Spain) to discover the changes elicited by the soil microbial communities and their functions along this gradient considering two microhabitats, under the canopy and open areas. Specifically, our objectives were (i) to analyse how soil properties (organic matter and pH), microbial communities (using PLFAs‐NFLAs) and enzymatic activities (related to C, N and P cycling) varied along the forest expansion gradient and between microhabitats and (ii) to decipher the pathways by which soil properties control the carbon and nutrient cycling in soils. The forest expansion gradient had a direct negative effect on phosphatase activity. The microhabitat showed a positive direct effect on organic matter content, pH, actinomycetes and arbuscular mycorrhizal fungi biomass and on soil C and P cycling. Moreover, the biomass of gram‐positive bacteria determined the biomass of other microbial groups. Synthesis and Applications: Though its effectiveness is variable, passive restoration can be more effective than active restoration. Our research indicates that passive tree colonisation of past agricultural land is enough to achieve soil functionality similar to a mature forest for most variables studied. However, some variables would need more time to reach mature forest levels, such as total microbial biomass and organic matter content. Therefore, to support ecosystem recovery, the management of this applied forest ecology strategy requires continuous monitoring of newly established trees and soil to elucidate the time needed to achieve mature soil properties. [ABSTRACT FROM AUTHOR]

The purpose of this paper was to analyze the possibility of changes in energy intensity of production in the context of farm investment scale. The empirical section relies on unpublished FADN microdata. The study answers the question of whether investments and fixed capital growth can contribute to improvements in environmental performance of agricultural production. As it turns out, the group of farms with the greatest amount of investments saw an increase in energy consumption costs, though at a much slower rate than growth in production value. As a result, there was a drop in energy intensity of production, defined as the ratio between energy costs and production value. These findings corroborate the hypothesis advanced in this study, namely that upon reaching a sufficiently large amount of investments, farms can become capable of reducing the energy intensity of their production activity. Hence, higher levels of capital productivity are attained when the farms invest in more energy-efficient fixed assets. Conclusions cannot be universal, as the analysis involved a portion of selected farms. However, the research indicates a trend in the study of energy intensity of production depending on the scale of investment and the validity of the investment comprehensiveness approach. These conclusions provide a basis for recommendations for an agricultural policy which should include support for investments that are large and comprehensive (in relation to the farm's assets) and go beyond the simple renewal of assets. Therefore, in its investment-oriented measures, the agricultural policy should take the innovativeness (including energy efficiency) criterion into account. [ABSTRACT FROM AUTHOR]

Farm aerial survey layers can assist in unmanned farm operations, such as planning paths and early warnings. To address the inefficiencies and high costs associated with traditional layer construction, this study proposes a high-precision instance segmentation algorithm based on SparseInst. Considering the structural characteristics of farm elements, this study introduces a multi-scale attention module (MSA) that leverages the properties of atrous convolution to expand the sensory field. It enhances spatial and channel feature weights, effectively improving segmentation accuracy for large-scale and complex targets in the farm through three parallel dense connections. A bottom-up aggregation path is added to the feature pyramid fusion network, enhancing the model's ability to perceive complex targets such as mechanized trails in farms. Coordinate attention blocks (CAs) are incorporated into the neck to capture richer contextual semantic information, enhancing farm aerial imagery scene recognition accuracy. To assess the proposed method, we compare it against existing mainstream object segmentation models, including the Mask R-CNN, Cascade–Mask, SOLOv2, and Condinst algorithms. The experimental results show that the improved model proposed in this study can be adapted to segment various complex targets in farms. The accuracy of the improved SparseInst model greatly exceeds that of Mask R-CNN and Cascade–Mask and is 10.8 and 12.8 percentage points better than the average accuracy of SOLOv2 and Condinst, respectively, with the smallest number of model parameters. The results show that the model can be used for real-time segmentation of targets under complex farm conditions. [ABSTRACT FROM AUTHOR]

Monitoring agricultural land over vast geographical areas presents challenges due to the absence of accurate, comprehensive and precise data, which has become a complex process that is difficult to do in terms of both timespans and consistency. Hence, this study presents an improved approach for the identification of agricultural land by utilizing the capabilities of Sentinel-1 and Sentinel-2 satellites with a variety of vegetation and non-vegetation indices and machine learning algorithms. The Multispectral Correlation Mapper (MCM) and Random Forest (RF) algorithms are adopted to train different agricultural lands, crop types and sowing and cultivation seasons. The 45-bands mega-file data cube (MFDC) fusion for each season incorporates essential indices and features derived from the Sentinel-1 and Sentinel-2 datasets for both seasons, i.e. Rabi (winter-spring season) and Kharif (summer-autumn season). The proposed method demonstrated resilience when applied to satellite datasets while effectively reducing the impact of non-agricultural elements such as shrubs, grass, bare soil and orchards. The results demonstrate a notable ability to differentiate between the Rabi and Kharif seasons, resulting in a high level of precision in measuring the extent of cultivated land during the Rabi and Kharif seasons with an area of 626,947 acres and 590,858 acres, respectively. The total land area, ascertained from the observation of the comprehensive cropping pattern and agricultural modifications during the entire year (June 2021–May 2022) is 635,655 acres. The validation exercise shows the higher accuracy of this method for cropland, with an overall accuracy of 98.8%, kappa of 0.97, user accuracy of 98.69% and producer accuracy of 99.13%. Additionally, it was spatially compared with the ESRI, ESA and MODIS cropland layers and government statistical data. Furthermore, the research investigates the temporal dynamics of agricultural growth phases using spectral bands and indices. This approach improves the accuracy of precise cropland identification and provides useful insights into crop phenology. [ABSTRACT FROM AUTHOR]

With an emphasis on the effects of climate change, this study offers a thorough GIS-based assessment of land use favorability in the Apuseni Mountains. The Apuseni Mountains, a region characterized by its biodiversity and complex terrain, are increasingly vulnerable to the impacts of climate change, which threaten both natural ecosystems and human activities. The territory of 11 territorial administrative units was selected for the investigation because it shows more of an anthropogenic influence due to the migration of people to mountainous areas following the COVID-19 pandemic, which increased the amount of anthropogenic pressure in this area. Factors that describe the climate of the study area, the soil characteristics, and the morphometric characteristics of the relief were used to create a classification for the present on classes of favorability and restrictiveness for the plots of land, using a quantitative GIS model to determine the favorability of the land for the main crops and agricultural uses. The current land favorability was thus initially obtained, taking into account current temperature and precipitation values and using the SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios for the 2020–2099 time frame. The results indicate a variation in the statistical classification of the land for different favorability classes, a decrease of 4.7% for the high favorability class for pastures, an estimated decrease of 4.4% for grassland, and in the case of orchards, the situation reflects a fluctuating variation. There is a decrease of 6.4% in the case of the very low favorability class according to SSP2-4.5 (in the case of reaching an average temperature of 12.7 °C and an annual precipitation of 895 mm), and in case of high and very high favorability, there is an increase in plots falling into better high favorability classes of up to 0.7%. [ABSTRACT FROM AUTHOR]

A precise land suitability assessment plays a vital role in modern agriculture to sustain production against population explosions worldwide without affecting natural resources or environmental factors. Further, recent urbanization has led to a reduction in arable land and forced the conversion of non-arable land to arable land. Therefore, developing land suitability with its potential and limitations is essential through advanced techniques like multi-criteria Decision Analysis for the particular area. The current research aimed to assess the land suitability of the Manimutha Nadhi watershed, Tamil Nadu, India, using geographical information system (GIS) and Analytic Hierarchy Process (AHP) methods. This study area, Manimutha Nadhi, covers a total area of 263.89 km2. Using a random sampling technique, 57 soil samples were obtained. The parameters for the AHP study were soil pH, EC, Soil organic carbon (SOC), available NPK, soil texture, and soil erosion. The climatic parameters, Annaula average rainfall (AAR) and Annual average temperature (AAT) and topographic parameter slope of the study area are also used as the criteria for generating final agricultural land suitability. The final suitability map through GIS and AHP was created using weighted overlay analysis. The land suitability class on the basis of FAO classification is highly suitable (S1), moderately suitable (S2), marginally suitable (S3), currently not suitable (N1), and permanently not suitable (N2) for agriculture. The results of the final land suitability map by AHP and GIS revealed that 150.58 km2 (57.09%) of the research region is highly suitable for agriculture (S1). The 29.60 km2 (10.08%) area was moderately suitable (S2) for irrigated agriculture. The 53.37 km2 (20.20%) area was marginally suitable (S3) for irrigated agriculture. The 10.56 km2 (7.53%) area was currently unsuitable (N1) for agricultural uses. The area of about 13.45 km2 (5.10%) was permanently unsuitable (N2) for agriculture. The outcomes can be used as baseline information on the land that could strengthen agriculture production in the Manimutha Nadhi watershed. [ABSTRACT FROM AUTHOR]

Food systems have been framed as a "wicked problem" due to the complex socio-ecological impacts they foster, ranging from contributing nearly a quarter of anthropogenic greenhouse gas emissions to a myriad of social impacts (e.g., health, food safety, and food security). In the European green transition for food systems, multiple actors are involved. However, farmers play a unique and critical role as agricultural land managers and navigators of social, political, and environmental factors. Using cover cropping and intercropping as examples, we illustrate the complexities arising when decision-making and governance at multiple levels lead to tradeoffs and unexpected consequences at the farm scale. Amid complexity, we propose a conceptual model to address the question: how is an agricultural green transition best fostered? We find that changes are incremental, transformative or both depending on the level of analysis. Additionally, incoherence in agronomic recommendations across academic disciplines and policy agendas creates challenges at the farm scale that trickle up and can thwart sustainable agricultural land use. Although transdisciplinarity and knowledge production with farmers through co-creation are essential for food system transformation and can be part of the solution, it is crucial to examine the nature of change processes and to consider how knowledge and innovation are adopted. By balancing top-down and bottom-up approaches and distributing burden from the farm scale to governance and food systems, a more transformative green transition for European food systems with coherence across multiple agroecological objectives could be achieved. [ABSTRACT FROM AUTHOR]

Sub-Saharan Africa's rapid urban growth is causing a decline in urban agricultural land, reducing access to healthy local food for citizens. This paper quantitatively and spatially describes the contribution of urban and peri-urban producers to the availability and accessibility of fresh leafy vegetables in the growing city of Dar es Salaam, Tanzania. Based on over 700 surveys with food vendors and consumers, as well as ethnographic research with farmers, we show how a total of 70 per cent of all amaranth consumed in Dar es Salaam was found to be produced within the city. With an average distance travelled from farm to fork of 11 km, leafy vegetables have a strongly localised foodshed. The results show the ongoing significance of urban areas in meeting their own food demands, emphasising the necessity to explicitly integrate food provisioning in the planning of growing African cities. [ABSTRACT FROM AUTHOR]

Potato Verticillium wilt (PVW) caused by Verticillium dahliae is a vascular disease, that seriously affects potato (Solanum tuberosum L.) yield and quality worldwide. V. dahliae occupies the vascular bundle and therefore it cannot efficiently be treated with fungicides. Further, the application of these pesticides causes serious environmental problems. Therefore, it is of great importance to find environmentally friendly biological control methods. In this study, bacterial strains were isolated from agricultural lands on which potato had been cultured for 5 years. Five strains with a broad-spectrum antagonistic activity were selected. Among these five strains, Bacillus velezensis XS142 showed the highest antagonistic activity. To study the mechanism of XS142, by which this strain might confer tolerance to V. dahliae in potato, the genome of strain XS142 was sequenced. This showed that its genome has a high level of sequence identity with the model strain B. velezensis FZB42 as the OrthoANI (Average Nucleotide Identity by Orthology) value is 98%. The fungal suppressing mechanisms of this model strain are well studied. Based on the genome comparison it can be predicted that XS142 has the potential to suppress the growth of V. dahliae by production of bacillomycin D, fengycin, and chitinase. Further, the transcriptomes of potatoes treated with XS142 were analyzed and this showed that XS142 does not induce ISR, but the expression of genes encoding peptides with antifungal activity. Here we showed that XS142 is an endophyte. Further, it is isolated from a field where potato had been cultured for several years. These properties give it a high potential to be used, in the future, as a biocontrol agent of PVW in agriculture. [ABSTRACT FROM AUTHOR]

Soil nutrient levels play a crucial role in determining crop yield. A comprehensive understanding of the spatial distribution patterns and evaluation grades of soil nutrients is of significant practical importance for informed fertilization practices, enhancing crop production, and optimizing agricultural land utilization. This study focuses on the urban area of Kashi Prefecture in Xinjiang as a case study. Utilizing soil sample data, GIS spatial interpolation analysis was conducted, incorporating plot boundary information to propose a comprehensive evaluation method for assessing soil nutrient levels at the plot level. Experimental findings revealed the following: (1) The average values of soil organic matter (SOM), total nitrogen (AN), total potassium (AK), and total phosphorus (AP) in the study area were determined to be 13.3 g/kg, 0.74 g/kg, 0.33 g/kg, and 0.03 g/kg, respectively. Among these, AN and SOM were classified as the fourth grade, indicating relatively deficient levels, while AK and AP were classified as the first and second grade, indicating relatively abundant levels. (2) The comprehensive evaluation of soil nutrient grades in the study area primarily fell within the third, fourth, and second grades, representing areas of 29.08 km2, 25 km2, and 4.05 km2, accounting for 50.03%, 43%, and 6.97% of the total area, respectively. (3) The evaluation results of soil nutrient levels at the plot level emphasized the boundary characteristics and provided a more refined assessment grade. This evaluation method is better suited to meet the practical production requirements of farmers and is considered feasible. The outcomes of this study can serve as a reference for precision agriculture management. [ABSTRACT FROM AUTHOR]

Co‐occurrence networks offer insights into the complexity of microbial interactions, particularly in highly diverse environments where direct observation is challenging. However, identifying the scale at which local and non‐local processes structure co‐occurrence networks remains challenging because it requires simultaneously analyzing network structure within and between local networks. In this context, the rumen microbiome is an excellent model system because each cow contains a physically confined microbial community, which is imperative for the host's livelihood and productivity. Employing the rumen microbiome of 1012 cows across seven European farms as our model system, we constructed and analyzed farm‐level co‐occurrence networks to reveal underlying microbial interaction patterns. Within each farm, microbes tended to close triangles but some microbial families were over‐represented while others under‐represented in these local interactions. Using stochastic block modeling we detected a group structure that reflected functional equivalence in co‐occurrence. Knowing the group composition in one farm provided significantly more information on the grouping in another farm than expected. Moreover, microbes strongly conserved co‐occurrence patterns across farms (also adjusted for phylogeny). We developed a meta‐co‐occurrence multilayer approach, which links farm‐level networks, to test scale signatures simultaneously at the farm and inter‐farm levels. Consistent with the comparison between groups, the multilayer network was not partitioned into clusters. This result was consistent even when artificially disconnecting farm‐level networks. Our results show a prominent signal of processes operating across farms to generate a non‐random, similar (yet not identical) co‐occurrence patterns. Comprehending the processes underlying rumen microbiome assembly can aid in developing strategies for its manipulation. More broadly, our results provide new evidence for the scale at which forces shape microbe co‐occurrence. Finally, the hypotheses‐based approach and methods we developed can be adopted in other systems to detect scale signatures in species interactions. [ABSTRACT FROM AUTHOR]