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• Lead toxicity was significantly reduced in rice plants by applying CeO 2 NPs and FeONPs. • Nanoparticle application caused significant reduction in MDA, H 2 O 2 , and electrolyte leakage. • Nanoparticles reduced Pb uptake in plants. • The growth of rice plants was stimulated by nanoparticle application. Nanotechnology offers creative and effective solutions for addressing various environmental issues, such as heavy metals (HM). The rapidly increasing HM concentrations in agricultural land have drawn considerable attention. Nanoparticles (NPs) have special physiochemical features that help reduce stress. This study assessed the viability of applying CeO 2 NPs and FeONPs to rice plants at a concentration of 25 mg/L to effectively remove the detrimental effects of lead (Pb) by using various concentrations (100 and 200 ppm) on plant development and growth. To achieve the desired concentrations, a Pb solution was prepared by dissolving lead nitrate in distilled water and added to the soil. Interestingly, the application of CeO 2 NPs and FeONPs resulted in a notable increase in plant growth, biomass, gas exchange characteristics, antioxidant enzymatic activity (SOD, POD, APX, and CAT), their gene expressions, as well as other antioxidants (phenols, prolines, amino acids, flavonoids, anthocyanins, and ascorbic acids) while simultaneously reducing oxidative stress (MDA, H 2 O 2 , and electrolyte leakage) and Pb uptake in rice. Conversely, Pb elevation in the soil increased oxidative damage and organic acid exudation pattern in the rice. At 200 ppm, a significant rise in Pb content (416.67 % and 380 %) was found in the roots and leaves of rice plants. According to our findings, rice growth can be bio-stimulated by CeO 2 NPs and FeONPs. Subsequent investigations should focus on the persistent ecological consequences and molecular mechanisms associated with the application of cerium oxide and iron oxide nanoparticles in agriculture to reduce Pb-induced oxidative stress. [ABSTRACT FROM AUTHOR]

Shangshan is the earliest Neolithic culture discovered in the lower Yangtze region and is significant as one of the origins of rice agriculture worldwide. During the Early Holocene, the Shangshan human groups settled in the river basins of the upper Qiantang region and transitioned from hunter-gatherers to farmers. Little is known about the dominant factors motivating them to settle down and their occupation patterns for adapting to local environments. To address these issues, we applied an ad hoc method by coupling landform classification, geomorphic fieldwork, accessibility analysis, and flood risk assessment. The results of geomorphic and accessibility analyses show that the ancient residents preferred to occupy the edges of the Middle Pleistocene terraces along second- and third-order streams. These locations are mainly situated in the centers of river basins and have height differences of 5–15 m, less than approximately 15-min walking distance from streams, providing them with stable residential spaces and accessible water resources. Within the 10-min territorial scope, there are always some fluvial plains near the terrace edges, indicating that the plain is another critical landform utilized by the residents. The wide plains and a plain-terrace-upland geomorphic assemblage, within 1-h and 2-h scopes, respectively, imply that the residents started to develop agricultural land while they also exploited diverse environments to ensure a plentiful food supply. Furthermore, the hydrologic analysis and assessment reveal that the residents were aware of the potential flood risks: their settlements are located at 30-min walking distance or greater from the high-order streams; most sites are exposed to low flood risks, with average percentages of unflooded territory greater than 67% and 43% in the minor and large flood scenarios, respectively; and each site has sufficient arable land with areas of 5–23 km2 within a 1-h scope, even under the large flood scenario. Overall, these results reveal that the river basins provided favorable living environments, and the residents could flexibly manage the landscapes to ensure stable settlements and food production, thus enabling the appearance and development of a sedentary society and rice agriculture during the early Holocene. Ultimately, the methods used in this study provide an effective approach to exploring the occupation patterns of prehistoric residents in the river basins. [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. Antimicrobial use (AMU) in Switzerland is above target and requires reduction, especially in dairy cattle. Measuring AMU is pivotal to identifying starting points for AMU reduction, and so are studies investigating its potential drivers in dairy farms worldwide. However, although AMU in dairy farms is high, studies estimating AMU specifically in tiestall farms are scarce. Tiestalls are a common housing system and their prevalence among dairy farms accounts to approximatively 73%, 41%, and 40% in Canada, the United States, and Switzerland, respectively. The objectives of this cross-sectional, retrospective observational study were to estimate AMU using the newly established Swiss national reporting system for AMU in livestock and to identify associated factors on Swiss tiestall dairy farms. We calculated the treatment incidence (TI) by using the European Medicines Agency methodology and their defined daily dose (DDD) and defined course dose (DCD) standards. Data on factors potentially associated with AMU were obtained through personal interviews with farm managers on 221 farms. Retrospectively, during a 1-year period, data on a total of 7,619 treatments were extracted from the national database. Associations between management factors and TI were analyzed using a generalized linear model with gamma distribution. The mean (± SD) overall TI was 5.46 DDD/cow-year (± 4.10 DDD/cow-year). Intramammary treatment during lactation accounted for highest TI (3.24 ± 3.16 DDD/cow-year), whereas dry-cow therapy accounted for lowest TI (0.44 ± 0.49 DCD/cow-year). We found that 5 of the investigated management factors were significantly associated with TI. Organic production (estimate = −2.16; 95% CI = −3.62 to −0.70) and herd size (estimate = −0.81; 95% CI = −1.23 to −0.39) were negatively associated with TI. Specific cow breeds (Brown Swiss estimate = 1.56; 95% CI = 0.45–2.68) and Holstein Friesian (estimate = 1.42; 95% CI = 0.03–2.82; reference: other breeds) and the use of hygienic powders on the lying area (estimate = 1.10; 95% CI = 0.04–2.17) were positively associated with TI. Overall, the Swiss national reporting system is a valuable tool for AMU estimation. Several herd characteristics and management factors were associated with AMU in tiestall farms. Further studies focusing on factors associated with AMU and which are amenable to intervention will help improve stewardship programs and subsequently reduce AMU in dairy cows. [ABSTRACT FROM AUTHOR]

The dissemination of antibiotic resistance genes (ARGs) in soil has become a global environmental issue. Vermicomposting is gaining prominence in agricultural practices as a soil amendment to improve soil quality. However, its impact on soil ARGs remains unclear when it occurs in farmland. We comprehensively explored the evolution and fate of ARGs and their hosts in the field soil profiles under vermicompost application for more than 3 years. Vermicompost application increased several ARG loads in soil environment but decreased the high-risk bla -ARGs (bla ampC , bla NDM , and bla GES-1) by log(0.04 – 0.43). ARGs in soil amended with vermicompost primarily occurred in topsoil (approximately 1.04-fold of unfertilized soil), but it is worth noting that their levels in the 40–60 cm soil layer were the same or even less than in the unfertilized soil. The microbial community structure changed in soil profiles after vermicompost application. Vermicompost application altered the microbial community structure in soil profiles, showing that the dominant bacteria (i.e., Proteobacteria, Actinobacteriota, Firmicutes) were decreased 2.62%–5.48% with the increase of soil depth. A network analysis further revealed that most of ARG dominant host bacteria did not migrate from surface soil to deep soil. In particular, those host bacteria harboring high-risk bla -ARGs were primarily concentrated in the surface soil. This study highlights a lower risk of the propagation of ARGs caused by vermicompost application and provides a novel approach to reduce and relieve the dissemination of ARGs derived from animals in agricultural production. [Display omitted] [ABSTRACT FROM AUTHOR]

The objective of this study was to describe the nutritional strategies used on Canadian dairy farms with automated milking systems (AMS), both at the feed bunk and the concentrate offered at the AMS, as well as to determine what dietary components and nutrients, as formulated, were associated with milk production and milking behaviors on those farms. Formulated diets (including ingredients and nutrient content) and AMS data were collected from April 1, 2019, until September 30, 2020, on 160 AMS farms (eastern Canada [East] = 8, Ontario [ON] = 76, Quebec [QC] = 22, and western Canada [West] = 54). Both partial mixed ration (PMR) and AMS concentrate samples were collected from May 1 to September 30, 2019, on 169 farms (East = 12, ON = 63, QC = 42, West = 52). We collected AMS milking data for 154 herds. For each farm (n = 161), milk recording data were collected and summarized by farm to calculate average milk yield and components. Multivariable regression models were used to associate herd-level formulated nutrient composition and feeding management practices with milk production and milking behavior. Milk yield (mean ± SD = 37.0 ± 0.3 kg/d) was positively associated with the PMR ether extract (EE) concentration (+0.97 kg/d per percentage point [ p.p. ] increase) and with farms that fed barley silage as their major forage source (n = 16; +2.18 kg/d) as compared with haylage (n = 42), whereas farms that fed corn silage (n = 96; +1.23 kg/d) tended to produce more milk than farms that fed haylage. Greater milk fat content (4.09 ± 0.28%) was associated with a greater PMR-to-AMS concentrate ratio (+0.02 p.p. per unit increase) and total diet net energy for lactation (+0.046 p.p. per 0.1 Mcal/kg increase), but a lesser percentage of NFC of the PMR (−0.016 p.p. per p.p. increase of NFC percentage). Milk protein content (3.38 ± 0.14%) was positively associated with the forage percentage of the PMR (+0.003 p.p. per p.p. increase of forage percentage) and the total diet starch percentage (+0.009 p.p. per p.p. increase of starch percentage), but was negatively associated with farms feeding corn silage (−0.1 p.p. compared with haylage) as their major forage. Greater milking frequency (2.77 ± 0.40 milkings/d) was observed on farms with free-flow cow traffic systems (+0.62 milkings/d) and was positively associated with feed push-up frequency (+0.013 milkings/d per additional feed push-up), but negatively associated with PMR NFC content and forage percentage of the total ration (−0.017 milkings/d per p.p. increase of forage percentage). Lastly, greater milking refusal frequency (1.49 ± 0.82 refusals/d) was observed on farms with free-flow cow traffic systems (+0.84 refusals/d) and farms feeding barley silage (+0.58 refusals/d) than with guided flow and farms feeding either corn silage or haylage, respectively. These data give insight into the ingredients, nutrient formulations and type of diets fed on AMS dairy farms across Canada and the association of those factors with milk production and milking behaviors. [ABSTRACT FROM AUTHOR]

Changes in policy and new plans can significantly influence land use and trigger land use change in the long term. The data for pre- and post-policy implementation is necessary to assess the specific policy's impact on land use. In the early nineties, Germany started promoting renewable energy production, including bioenergy, which changed the agricultural landscape. Remote sensing (RS) image-based machine learning models can be beneficial for mapping agricultural land use in the present and the past. However, machine learning classification models trained on RS data from specific training sites and time may not be able to predict data for unknown sites and unknown temporal points due to changes in crop phenology, field features, or ecological site circumstances because most of the models are limited in their performances according to variations of the training data set. Therefore, this study aims to assess the spatial–temporal transferability of Landsat-based agricultural land use type classification. The study was developed to map agricultural land cover (5 classes: maize, grasslands, summer crops, winter crops, and mixed crops) in two regions in Germany (North Hesse and Weser-Ems) between the years 2010 and 2018 using Landsat archive data (i.e., Landsat 5, 7, and 8). Two machine learning models (random forest − RF and 2D convolution neural network – 2DCNN) were trained and evaluated according to no transferability (reference) scenario and three spatial–temporal scenarios using mF1 and class level F1 values. Three model transferability scenarios were evaluated: a) temporal – S1, b) spatial – S2, and c) spatiotemporal – S3. The reference scenario, without transferability, achieved an overall accuracy of 89.1% and a macro F1 score of 0.74 for RF and 89.9% and 0.75 for CNN, respectively. Under three transferability scenarios (S1, S2, and S3), the macro F1 scores decreased to 0.67, 0.66, and 0.62 for RF, and 0.68, 0.62, and 0.58 for CNN, respectively. The dissimilarity between the data employed to train the model and data from the new domain indicated a clear link that could explain the reduction in model predictability. Moreover, the performance degradation could be attributed to the disparity in environmental, climatic, and crop calendar conditions between the two domains. Understanding the extent of model performance degradation during transferability is crucial for developing effective strategies to mitigate these issues and enhance the generalisability of machine learning models for agriculture land cover mapping. [ABSTRACT FROM AUTHOR]

Cropland non-agriculturalization (CNA) refers to the conversion of cropland into construction land, woodland/garden/grassland, water body, or other non-agricultural land, which ultimately disrupts local agroecosystems and the cultivation and production of crops. Remote sensing technology is an important tool for large-area CNA detection, and remote sensing based methods that can be used for this task include the time-series analysis method and change detection from bi-temporal images. In particular, change detection methods using high-resolution remote sensing imagery have great potential for CNA detection, but enormous challenges do still remain. The large intra-class variance of cropland with different phenological stages and planting patterns leads to cropland areas being difficult to identify effectively, while certain features can be misidentified because they are similar to cropland, resulting in false alarms and missed detections in the results. There is also a lack of large-scale CNA datasets covering multiple change scenarios as data support. To address these problems, a lightweight model focused on CNA detection (CNANet) is proposed in this paper. Specifically, the uniquely crafted represent-consist-enhance (RCE) module is seamlessly integrated between the encoder and decoder components of CNANet to perform a contrast operation on the deep features extracted by the feature extractor. The RCE module is specifically designed to aggregate multiple cropland representations and extend the cropland representations from the confusing background, to achieve the purpose of reducing the intra-class reflectance differences and enhancing the model's perception of cropland. In addition, a large-scale high-resolution cropland non-agriculturalization (Hi-CNA) dataset was built for the CNA identification task, with a total of 6797 pairs of 512 × 512 images with semantic annotations. Compared to the existing datasets, the Hi-CNA dataset has the advantages of multiple phenological stages, multiple change scenarios, and multiple annotation types, in addition to the large data volume. The experimental results obtained in this study show that the benchmark methods tested on the Hi-CNA dataset can all achieve a good accuracy, proving the high-quality annotation of the dataset. The overall accuracy and F1-score of CNANet with the default settings reach 93.81 % and 78.9 %, respectively, achieving a superior accuracy, compared to the other benchmark methods, and demonstrating stronger perception of cropland changes. In addition, in two selected verification regions within the large-scale real-world CNA mapping results, the F1-score is 83.61 % and 50.87 %. The Hi-CNA can be downloaded from http://rsidea.whu.edu.cn/Hi-CNA_dataset.htm. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems-Interferometric Reflectometry (GNSS-IR) is an emerging remote sensing technique studied and demonstrated for soil moisture retrieval over bare soil using multipath GNSS data. However, retrieval of soil moisture in the presence of vegetation/crops is still a very challenging task even becoming a more complex problem without using any ancillary datasets to compensate the vegetation effect for soil moisture retrievals in higher crop growth stages. This paper presents a novel multipath phase-based vegetation correction scheme for improved field-scale soil moisture retrieval using L-band data from Navigation with Indian Constellations (NavIC) based on GNSS-IR technique. The proposed three steps vegetation correction scheme categorized the crop as per growth stages for sensitivity analysis of NavIC derived multipath phase as GNSS-IR observables and significantly compensated vegetation effects on multipath phase for improved soil moisture retrievals. The proposed method utilized only multipath phase data as single SNR metrics without any ancillary data sets and validated during complete growth cycle of winter wheat crop (sowing to harvesting stages). It has been shown that the soil moisture retrieved using proposed vegetation correction scheme provides a Pearson correlation coefficient of 0.87 and ubRMSD of 0.0341 m3/m3 with in situ measured soil moisture, which is better than bare scheme with Pearson correlation coefficient of 0.71 and ubRMSD of 0.0480 m3/m3. Overall, proposed scheme has found highly effective for vegetation correction and has shown a good potential candidate for VSM retrievals over crop-covered soil using other GNSS constellations (GPS, GLONASS, Galileo and BeiDou etc.). [ABSTRACT FROM AUTHOR]

Smart farming is a new concept that can be described as applying different kinds of modern technology to make farming more efficient. One such technology is the Internet of Things, an extension of the Internet and other connections into mundane devices. This paper uses dedicated sensors and cloud computing technologies to model a smart farming system that helps farmers keep track of state of the crops and livestock. It also covers implementing and testing an Android tracker app that is supposed to become the first component of the mentioned system, a client application that communicates with the cloud computing services yet to be implemented. The end product provides its users with an easy way of keeping track of the state of their farms, simplifying the farmers' work and enhancing productivity. [ABSTRACT FROM AUTHOR]

Urban population growth in Turkey has been increasing rapidly since the middle of the 20th century. The escalating population, particularly in urban regions, has emerged as a critical challenge in achieving sustainable urban management. The Sustainable Development Goals (SDG) 2, 9, and 11 present various avenues for governments to adress this predicament. This study employs the framework of multiple stream model, offering a valuable lens through which policies can identify opportune moments for policy implementation. The study examines the impact of industrialization and agricultural land use on the country's urban population while identifying potential opportunities for enhancing sustainable urban management in alignment with the SDGs. Subsequently, it critically examines the asymmetrical effects of these two significant factors on the urban population, formulating policy recommendations based on the empirical findings. The study concludes by stressing the significance of prioritizing sustainable, long-term agricultural and industrialization policies outlined in national policy documents rather than relying on short-term and temporary solutions. [ABSTRACT FROM AUTHOR]

Chile is a country that has had large renewable energy resources in recent years, and it has demonstrated a commitment to the use of clean energy. However, most solar energy projects are located in northern Chile, neglecting other areas, such as the southern part of the country, where the photovoltaic energy industry has the potential to thrive. This is the case in the Araucanía region, particularly in Temuco, which has diverse agricultural activity. However, being a remote area without access to the electrical grid, it relies on generation sources like oil. This article, a research product, aims to demonstrate the functionality that can be added to agricultural land by combining it with solar parks. To achieve this, an agrovoltaic system was estimated for a plot of land in the agricultural zone of Temuco. This system will serve as a sample of the integration between photovoltaic infrastructure and the agricultural industry for those interested in installing it. [ABSTRACT FROM AUTHOR]

Organic and conventional farms often coexist, yet their proximity does not ensure compatibility. Larsen et al. reveal that being surrounded by organic fields reduces pesticide usage in organic fields but increases it in conventional fields. We discuss these findings, emphasizing the need to cluster organic croplands for reduced pesticide use. [ABSTRACT FROM AUTHOR]

This study investigates the impact of Payments for Watershed Ecosystem Services (PWS) on the sustainable utilisation of cropland and crop security in Huangshan City, which is the primary implementation area for the Payments for Xin'an River Watershed Ecosystem Services pilot in China. Using a two-stage analytical framework along with a quasi-natural experiment, the research examines the policy's effectiveness in Huangshan City. In the initial stage, the directional distance function and global Malmquist-Luenberger index (DDF-GML) model are employed to calculate the agricultural green total factor productivity (AGTFP), representing sustainable management of cultivated land. Concurrently, the study constructs unobservable counterfactual paths using the synthetic control method (SCM) to evaluate impacts on land use practices. The findings show an increase in Huangshan City's AGTFP from 1.118 in 2010 to 1.823 in 2020. Quantitative assessments indicate that the policy contributed to a 15.3% rise in AGTFP in the PWS cohort compared to the control group. Though initial decreases were detected during three pilot intervals, a consistent upward trajectory in AGTFP was identified, suggesting that PWS encourages crop stability and the sustainable development of arable land. Further support from permutation, regional, and temporal placebo examinations affirm these conclusions. This work implies that PWS may be extended to other agriculturally vulnerable regions to foster a balanced alignment between sustainable growth and the enhancement of crop security The assessment focuses on policy lessons that could inform broader sustainability strategies. [ABSTRACT FROM AUTHOR]

The area adjacent to the milking parlor, accessible for grazing by lactating dairy cows (i.e., the grazing platform [GP]), can be limited on fragmented pasture-based dairy farms. Such farms, with a moderate overall farm stocking rate, typically have a much higher stocking rate of dairy cows on the GP. This study quantified the effects of farm fragmentation on milk and herbage production and profitability in a whole-farm systems-scale study over 3 yr (2017–2019). Four systems, each with an overall farm stocking rate of 2.5 cows/ha but with different grazing platform stocking rates (GPSR), were examined. The proportions of the overall farm area within the GP were 100%, 83%, 71%, and 63% in each of the 4 systems, respectively. Hence, the 4 systems had a GPSR of 2.5, 3.0, 3.5, and 4.0 cows/ha. The GP was used for grazing and silage (ensiled herbage) production, and the non-GP portion of each GPSR system was used solely for silage production. Concentrate supplementation per cow was the same across all GPSR systems; approximately 10% of the annual feed budget. All systems were compact spring-calving with 24 cows per system. We discovered a lower proportion of grazed herbage in the diet with higher GPSR. All silage produced on the non-GP areas was required to support higher GPSR on each of the systems. Annual herbage production and milk production per cow were not different between GPSR systems, resulting in similar milk production per hectare of the overall system area. The economic implications of different GPSR on fragmented farms were modeled in 2 scenarios: (1) quantifying the cost associated with different levels of farm area fragmentation; (2) investigating the optimum GPSR on fragmented pasture-based dairy farms, depending on variable criteria. A greater level of farm fragmentation lowered the profitability of pasture-based dairy production. Costs of production increased with higher GPSR and longer distances between GP and non-GP areas. At a fixed GP area, it was most profitable to increase GPSR up to 4 cows/ha on the GP when milk price was high, land rental price was low, and shorter distance existed between GP and non-GP areas. [ABSTRACT FROM AUTHOR]

As an important agricultural facility that supports efficient crop production and precise water management, center-pivot irrigated cropland (CPIC) plays a critical role in increasing crop yield and ensuring food security globally. Since the 1980s, CPIC has expanded significantly and has become one of the most valuable irrigated croplands in Brazil. However, national-scale and long-term CPIC products are currently unavailable, making spatiotemporal analysis for related ecological and economic factors challenging. In this study, we proposed a novel framework combining cloud platforms and deep learning algorithms to produce annual CPIC maps (ACMs) with 30 m spatial resolution in Brazil from 1985 to 2021. Leveraging all available Landsat-5, 7, and 8 images in Brazil, we generated three-band annual composite images to obtain stable image features for CPIC and alleviate the heterogeneity of CPIC. Then we constructed a large-scale training dataset covering different climatic zones of Brazil and spanning multiple years. Based on this dataset a CNN model was trained and applied to the whole of Brazil and generalized to other years. We deployed the CNN model to the cloud platform and combined it with Google Earth Engine to produce Brazil's annual CPIC Maps (ACMs). Moreover, we analyzed the spatiotemporal dynamics of CPIC and explored the impact of CPIC expansion on forests. The accuracy assessment demonstrated that our ACMs reached an average mIoU of 0.947 ± 0.021 for all study years, and the estimated CPIC area was highly consistent with official statistics. Our results indicated that Brazilian CPICs reached 1.8 × 106 ha in 2021, which was 45 times more than that in 1985. CPIC expansion occurred mainly in the states of Minas Gerais, Goiás, and Bahia over the past few decades, with an average annual increase in area of over 8,000 ha. We also found that CPIC expansion had encroached on 42,000 ha of shrubs and 25,000 ha of forests since 1985, and the impact was continuing and intensifying. To the best of our knowledge, the produced ACMs are the longest and most spatially detailed CPIC products in Brazil that reflect the CPIC spatiotemporal changes in the last four decades and can provide essential information services for Brazil's water resource management and agricultural decision-making. [ABSTRACT FROM AUTHOR]

This study evaluated the associations between estimated distance from farms' locations to auction markets, and health indicators of surplus dairy calves sold during summer 2019 and winter 2020 in Québec, Canada. A total of 3,610 animals from 1,331 different farms were used in this cross-sectional cohort study. Geographic coordinates (latitude and longitude) were obtained for each farm and the 2 participating livestock auction markets. Calves' abnormal physical signs (APS) were noted upon arrival at the auction market as they were examined by trained research staff. The haversine distance between the farm and the auction market was evaluated using geographic coordinates and categorized. Generalized linear mixed models were used for statistical analyses. The main APS observed were ocular discharge (34.9%), abnormal hide cleanliness (21.2%), swollen navel (17.2%), dehydration score 1 (at least one of the 2 following clinical signs: persistent skin tent or sunken eye, 12.9%), and dehydration score 2 (both clinical signs mentioned above, 6.5%). Calves from farms located at greater distances from the auction markets (≥110 km) had a higher risk ratio [RR = 1.08; 95% confidence internal (CI) = 1.03, 1.13] for dehydration than those from lesser distances (0–25 km). During the summertime, a RR of 1.18 (95% CI = 1.15, 1.22) was observed for dehydration compared with wintertime. A 2-way interaction between estimated distance and season showed a higher prevalence of ocular discharge for calves from farms at distances greater than or equal to 110 km during the summer (RR = 1.11; 95% CI: 1.04, 1.20) than for calves from farms located at lesser distances (0–25 km). These results demonstrate that calves from farms located at greater distances from the auction markets had more APS, mainly during the summer. A better understanding of the transport conditions and interaction with management at the farm of origin is determinant to mitigate the impact of the journey on surplus calf health. [ABSTRACT FROM AUTHOR]

[Display omitted] Knowledge of the extent and distribution of irrigated and rainfed croplands is critical in providing the necessary baseline data for enhancing agricultural efficiency and making informed policy decisions. Accurately identifying and mapping irrigated and rainfed croplands can hasten the attainment of Sustainable Development Goals (SDGs) 1 and 2, aimed at reducing poverty and hunger, respectively. However, traditional methods employed to identify and map cropland areas are expensive and require substantial labour, particularly in extensive environments. As a result, this study presents a comprehensive and spatially explicit methodological framework for identifying and mapping national-scale irrigated and rainfed croplands in South Africa. This framework leverages low-cost earth observation technologies (Sentinel-2 MSI) and employs highly accurate classification algorithms, namely Deep Learning Neural Network (DNN) and Random Forest (RF). The proposed methodology strategically integrates data from multiple sources, including public repositories (e.g., cropland data, evapotranspiration), ongoing research (e.g., land cover maps), and field data, to enhance the accuracy and reliability of the results. The methodology begins by employing a robust random forest model to classify the study area into distinct land cover types. Leveraging the power of a deep learning neural network (DNN), the method accurately distinguishes between irrigated and rainfed croplands in South Africa. The random forest model achieved a notable classification accuracy of 0.77 when identifying the main land-use and land cover types. Meanwhile, the deep learning neural network (DNN) model achieved an accuracy of 0.71 in differentiating rainfed and irrigated croplands at a national scale. These results highlight the effectiveness of the proposed methodology in providing baseline information relevant to crop monitoring, yield forecasting, and understanding agricultural food supply systems. Furthermore, the proposed methodology has the potential to offer timely and accurate information on cropland areas and their extent which could assist in implementing targeted interventions for optimising agricultural productivity. With its potential to be upscaled to other sub-Saharan countries, this methodology enriches agricultural decision-making and plays a vital role in bolstering food security and advancing the attainment of SDGs. [ABSTRACT FROM AUTHOR]

This research presents a solution that combines deep learning-based image processing, blockchain technology, and the Internet of Things (IoT) to achieve smarter control and traceability in greenhouse operations within the agricultural sector. By integrating these technologies, the aim is to overcome challenges posed by climate change, plant growth, limited agricultural land, and water scarcity, while enhancing crop yields and ensuring efficient and secure operations. The proposed system automates image capture, measurement, storage, and monitoring of environmental parameters in greenhouses, utilizing highly accurate image processing techniques with a 98% success rate. The integration of blockchain technology establishes an immutable and transparent record of transactions and data points, thereby improving traceability across the agricultural supply chain. This comprehensive approach fosters accountability, transparency, and trust, empowering consumers to make well-informed decisions regarding the products they purchase. Ultimately, this research contributes to advancing efficient and sustainable agricultural practices. [ABSTRACT FROM AUTHOR]

Public concern about the welfare of farm animals and the livestock industry's awareness of the need to improve animal welfare have led to the implementation of new tools designed to meet this objective. Especially in the case of small-scale farms in marginalized areas, the lack of available data specifically for assessing welfare led to the present study. Its aim was to use animal- and resource-based indicators that are representative of small-scale farms, to establish an index and develop a benchmarking tool that can be used to dynamically evaluate the welfare of cattle on farms with different husbandry systems and provide farmers, veterinarians, extension officers, and stakeholders with reliable information that can be used as a decision support tool. For this reason, 1,891 cows from 204 herds housed in freestall (n = 111) and tiestall (n = 93) husbandry systems in South Tyrol (Northern Italy) were evaluated. The results showed that 17.6% of herds (36 farms) had an average score below 60 (out of 100), which means that immediate intervention should be adopted to improve welfare. South Tyrolean dairy farms performed well in terms of lameness, skin alterations, avoidance behavior, number and space of lying areas, and dystocia occurrence scores, whereas water supply, cow cleanliness status, claw conformation, and getting-up behavior offered significant potential for improvement. Significant differences were observed between housing systems scoring less than 60 out of 100 points in 9.9% (n = 11) and 26.9%, (n = 25), of the herds kept in freestalls and tiestalls, respectively. A slight difference was found when comparing the percentage of herds with high scores, which corresponded to 30.6% (n = 34) of herds housed in freestalls and 22.6% (n = 21) of herds housed in tiestall housing systems. In addition, it was shown that animals reared on farms with tiestalls more often had problems with body condition, cleanliness, and skin alterations. All individual welfare indicators in the top 25% of herds had scores above 80, indicating that this may be an attainable target for freestall and tiestall herds, respectively, to promote good dairy cow welfare. The overall aim must be to adopt measures to increase the scores on all farms closer to this level. [ABSTRACT FROM AUTHOR]

We analyze the impact of a 2021 reform in Ukraine that—after earlier digitization efforts did not produce desired results—mandated use of transparent online auctions by local governments rather than a central agency to lease rights to public agricultural land. The shift to a collusion-proof electronic auction system increased lease revenue by 175% for our preferred specification. Had all public land that Ukraine transferred since 2015 been auctioned using post-reform mechanisms, local governments would receive incremental lease revenue of US$500 million per year for the duration of the lease. Where public land is important, reforms to ensure rights to such land are allocated transparently, competitively and in a decentralized way could improve social, economic, and environmental outcomes. [ABSTRACT FROM AUTHOR]

Agrivoltaics is a technology aiming to combine renewable energy generation and agricultural production and, as such, has great potential to address land use conflicts. Understanding how farmers perceive the opportunities and barriers to Agrivoltaics implementation is fundamental for stakeholder participation and social acceptance. However, the research on social acceptance of Agrivoltaics technologies has primarily focused on the United States and Europe. These studies have shown that identifying factors underlying stakeholders' perceptions and the surrounding legal framework at an early stage is important for the successful and socially responsible adoption of the technology. In this first study on Turkish farmers' perspectives on Agrivoltaics, we use in-depth semi-structured interviews to explore pioneer farmers' perceptions of the opportunities and challenges in Agrivoltaics. This is also the first study investigating farmers' perspectives with an explicit focus on how they relate to the institutional setting of agricultural land use policy, which we explore by extensive desk research and interviews with the agricultural bureaucracy. The pioneer farmers exhibit an overall positive attitude towards Agrivoltaics by identifying and valuing the synergistic potential of Agrivoltaics systems. In particular, they are perceptive about how they may use Agrivoltaics to solve local problems, including those exacerbated by input dependency and climate change, beyond an abstract opportunity dimension. Despite this solid motivational drive for Agrivoltaics, however, pioneer farmers' concerns about potential bureaucratic hassle as well as shortcomings in the current legislation indicate a weak institutional setting undermining viability of Agrivoltaics implementation. Agricultural bureaucracy's distrust of potential investors and users seem to reflect a serious concern for 'pseudo-agriculture,' caused by both low procedural capacity and lack of institutional coordination (among regulatory institutions in Energy and Agriculture). This mistrust, in return, explains farmers' negative experiences, such as red tape in receiving licenses and permits for non-dual renewable energy applications (for agricultural purposes) under current legislative framework, contributing also to their doubts about sustained government support for future dual-use applications. Understanding this institutional setting can support policy makers' decisions on how to align renewable energy investments with agricultural need and purposes. • Semi-structured interviews with the pioneer farmers in Turkiye. • Strong positive perception of Agrivoltaics among pioneer farmers. • Among perceived benefits are cost savings and yield stability given climate risks. • Among perceived drawbacks are financial, operational and institutional problems. • The institutional setting—mutual mistrust between actors—undermines viability. [ABSTRACT FROM AUTHOR]

Soil Organic Carbon (SOC) is amongst others an indicator for soil degradation and soil health of croplands. Induced by recent policy initiatives, awareness for high resolution SOC maps and techniques to estimate changes is increasing. For area-wide mapping approaches with at least a field resolution, Earth Observation is a valuable data source to extract bare soil areas and to quantify SOC contents for these areas. In this context, compositing techniques of multi-temporal image archives are widely used to overcome the limitation of vegetation cover of fields during the overpass of the satellite. Comparing current bare soil compositing approaches, two aspects are of particular importance: 1) the index for bare soil selection and 2) the length of the compositing period for recurrent analyses. In this study, we applied the Soil Composite Mapping Processor (SCMaP) to the full archive of Landsat data between 2005 and 2019 to optimize parameters for soil reflectance composite (SRC) generation of multitemporal satellite imagery for SOC predictions. For this purpose, three spectral indices (PV + BLUE, PV + IR2, and NBR2) for SRC generation were implemented in the SCMaP chain. For all three indices a validation of the extracted bare soil dates with field observations and phenological information from the crop calendar showed a reliable extraction of bare soil dates. Due to the crops in the investigation area, spring and autumn months indicated the highest proportion of correctly selected bare soil dates. We also analyzed the SOC modeling capabilities of different composed SRCs (indices and varying seasonal and temporal lengths) in combination with available legacy data. In comparison to a seasonal pre-selection of scenes (spring and autumn months) included in the SRC, the different indices showed a minor influence on SOC modeling. However, PV + BLUE performed best (R2: 0.56 – 0.72, RMSE: 1.09 – 1.29%, RPD: 1.51 – 1.91). Furthermore, we compared the SOC model capabilities for different SRC compositing lengths (3-, 5-, 7-, 10- and 15-years). For PV + BLUE and PV + IR2, longer compositing lengths (from three to 15 years) resulted in an increase of the model accuracies and performances. However, for NBR2 this was not as clear. Based on the results at least a 5-year compositing period is required for recurrent SOC predictions using Landsat data. [ABSTRACT FROM AUTHOR]

• A novel method is proposed to find the suitable locations for large-scale PV farms. • The method ranks suitable locations by determining sequential optimum locations. • Criteria weights are adjusted based on PV plant capacity. • The land of cost is included as a criterion in the decision process. Effective and sustainable climate-friendly policies to reduce carbon dioxide emissions in line with the 2020 European Green Deal are necessary. Accordingly, renewable energies assumed a critical role, rendering the site selection of these systems very crucial. The present study proposes a novel approach to the site selection of large-scale photovoltaic (PV) plants using a combination of analytic hierarchy process (AHP) and geographic information system (GIS). In the study, the weights of criteria used for selecting solar PV panels are adjusted according to the installed capacity of the PV plant. The land of cost is included as a criterion in the AHP for the first time in PV plant site selection. Besides, a novel method called optimality-based site growing (OBSG) is introduced to further analyze the suitable sites obtained from GIS simulations and to determine the most suitable locations of PV farms. The proposed method is demonstrated with a case study of Türkiye, and the results show that the method effectively determines the most suitable locations for large-scale PV plants. [ABSTRACT FROM AUTHOR]

Rising demand for protein-rich foods can impact N 2 O emissions from croplands. Recent research has pointed to the role of modified plant vasculature in grain protein increase. Here we highlight how discovering the mechanistic role of plant vasculature in protein improvement and nitrogen-use efficiency could reduce global N 2 O emissions. [ABSTRACT FROM AUTHOR]

The article presents the methodology of applying factor analysis. The purpose of the research is to examine the applicability of factor analysis in the assessment of factors determining the return on equity of farms in the European Union, aimed at reducing the correlated variables without losing the information contained therein regarding the return on equity. The research used data published in the European FADN (Farm Accountancy Data Network). Factor analysis was performed with the R statistical program, ver. 4.2.2. On the basis of the conducted research, 4 factors were identified, into which 15 variables were classified. The factors were given the following names: Farm size and production potential (factor 1), Income and ability to self-finance operations (factor 2), Effects of long-term investment and financial decisions (factor 3) and Investment activity (factor 4). The first factor includes: Labor, Area, Subsidies, S_loans, Inputs, Output, Current_assets and Size. To the second: Income, Cash_flow 2 and Cash_flow 2. The third factor includes Fixed_assets and LM_loans, while the fourth one includes Net_inv and Gross_inv. The use of factor analysis allowed the reduction of variables and the identification of factors determining the return on equity of farms from European Union countries. [ABSTRACT FROM AUTHOR]

Accurate information on crop type spatial distributions is crucial for yield estimation, agroecological modeling, and agrarian policy development. In comparison with moderate resolution (10–30 m) satellite images, very high-resolution (VHR, ≤ 1 m) images are superior at describing the spatial details and complex texture features of agricultural land. However, VHR images generally suffer from insufficient spatiotemporal coverage and high spectral variation in crop type, making fine-scale crop type mapping at regional or larger scales challenging. In this study, we developed a novel data-driven residual attention U-shape network (RAUNet) to identify crop types at the regional scale based on VHR images. RAUNet was adapted from UNet by introducing a residual module and a gated attention mechanism to strengthen the multilevel representation of crop features from VHR images. We selected rice-crayfish fields (RCFs) in Hubei Province, China, as the case crop type and area to test the performance of RAUNet. Seven models, including RAUNet pan , ResUNet, AttnUNet, UNet, DeepLabV3 + and random forest at the pixel and object levels, were adopted for comparison with RAUNet. Moreover, the model transferability was evaluated over regions with different agricultural landscapes. Our results showed that RAUNet-derived RCF maps obtained an average F1-score and MCC of 0.90 and 0.85 at different cultivation stages, which significantly outperformed other methods. Additionally, RAUNet had good spatiotemporal transferability, with an average F1-score and MCC of 0.93 and 0.88 in four transfer areas. The visualization results of the attention weights indicated RAUNet's prominent capability in combining useful and relevant information from different feature levels to produce refinement predictions for the target crop type. Furthermore, the high-level features derived from RAUNet can well separate RCFs and non-RCFs over different landscapes, which were also consistent across different cultivation periods. These encouraging results suggest that RAUNet works well for fine-scale crop type mapping in smallholder farming systems, and it is an efficient method for land cover mapping based on VHR images at the regional scale. [ABSTRACT FROM AUTHOR]

[Display omitted] The use of remote sensing data methods is affordable for the mapping of soil properties of the plowed layer over croplands. Carried out in the framework of the ongoing STEROPES project of the European Joint H2020 Program SOIL, this work is focused on the feasibility of Sentinel-2 based approaches for the high resolution mapping of topsoil clay and organic carbon (SOC) contents at the within-farm or within-field scales, for cropland sites of contrasted climates and soil types across the Northern hemisphere. Four pixelwise temporal mosaicking methods, using a two years-Sentinel-2 time series and several spectral indices (NDVI, NBR2, BSI, S2WI), were developed and compared for i) pure bare soil condition (maxBSI), ii) driest soil condition (minS2WI), iii) average bare soil condition (Median) and iv) dry soil conditions excluding extreme reflectance values (R90). Three spectral modeling approaches, using the Sentinel-2 bands of the output temporal mosaics as covariates, were tested and compared: (i) Quantile Regression Forest (QRF) algorithm; (ii) QRF adding longitude and latitude as covariates (QRFxy); (iii) a hybrid approach, Linear Mixed Effect Model (LMEM), that includes spatial autocorrelation of the soil properties. We tested pairs of mosaic and spectral approaches on ten sites in Türkiye, Italy, Lithuania, and USA where soil samples were collected and SOC and clay content were measured in the lab. The average RPIQ of the best performances among the test sites was 2.50 both for SOC (RMSE = 0.15%) and clay (RMSE = 3.3%). Both accuracy level and uncertainty were mainly influenced by site characteristics of cloud frequency, soil types and management. Generally, the models including a spatial component (QRFxy and LMEM) were the best performing, while the best spatial mosaicking approaches mostly were Median and R90. The most frequent optimal combination of mosaicking and model type was Median or R90 and QRFxy for SOC, and R90 and LMEM for clay estimation. [ABSTRACT FROM AUTHOR]

Habitat conversion to agricultural land is one of the main threats to terrestrial biodiversity and can affect ecosystem processes and cause changes in ecosystem services (ESs) and disservices (EDs). Yet, studies often rely only on the abundance and diversity of the service providers; the effects on ecological processes of habitat conversion are rarely directly monitored. In this study, we used the sentinel approach to evaluate how habitat conversion from native forest to agricultural land affected ESs and EDs on an oceanic island. We quantified herbivory on lettuce plants, invertebrate and vertebrate predation rates on artificial caterpillars, pollination on strawberry plants, seed predation on wheat and mustard seeds, and leaf decomposition rates in native forests, maize fields and pastures on Terceira Island, Azores (Portugal). Herbivory, invertebrate predation rates, and pollination service were not significantly different between habitats. Vertebrate predation rates in native forests (mean 6.1% d−1) were significantly higher than that in pastures (0.3% d−1), or high-elevation maize fields (0.5% d−1), and marginally higher than in low-elevation maize fields (2.2% d−1). Overall seed predation after 48 h was significantly higher on wheat (mean 16.8%) than mustard seeds (5.6%). High-elevation maize fields also had higher seed predation (27.8%) than low-elevation ones (0.6%) or pastures (3.6%), but did not differ from the native forest (12.9%). Decomposition after 90 days was highest in pastures (78.4% and 45.9%, for tea and rooibos, respectively); although no significant differences between habitats were detected, except for low-elevation maize fields (64.4% and 33.6%). Conversion from native forest to cultivated land did not cause a clear decrease in the intensity of the studied ESs/EDs except for vertebrate predation. Using direct monitoring tools to simultaneously and consistently quantify multiple ecological processes is not only possible but needed, as ecological processes can respond differently to landscape changes. [Display omitted] • Ecological processes were assessed after converting Azorean forests to agriculture. • All quantified ecosystem services/disservices showed idiosyncratic changes. • Predation rates significantly decreased when forest was converted to cropland. • Herbivory and pollination did not indicate significant changes. • Decomposition and seed predation increased in high elevation maize fields and pastures. [ABSTRACT FROM AUTHOR]

Rural areas have great renewable energy potential. With an introduction to sustainable development goals, the smart farm concept presents a novel idea for providing energy in rural areas using artificial intelligence and renewable energy management. We proposed the following topics in this research: (i) methodologies for sizing generation systems by integer linear programming, (ii) use of analytic hierarchy process (AHP) to select the alternative source by financial, environmental, social, and physical criteria, and (iii) training of an artificial neural network (ANN) for process optimization based on the agroeconomic profile of farms. We applied the proposed generic methodology to farms in São Francisco do Glória, Brazil. The biomass, solar, and wind systems were indicated by AHP for implementation in 62.16, 15.32, and 22.52% of farms, respectively. The best configuration of ANN presented a maximum precision of 81.80 ± 3.36%. If the systems were to be implemented, 1.975 GWh yr−1 would be generated, 435.23 tonnes of CO 2 would no longer be emitted per year, and a CO 2 credit of 366.62 tonnes yr−1 would be injected into the national electric system. Public policies are necessary for this scenario to become a reality in Brazil, such as research incentives and market development. [Display omitted] • Three power generation systems for farms in a small municipality were evaluated. • A decision-making tool was developed considering four indicators. • Economic, financial, social, and physical indicators were used. • Biomass was indicated as an alternative source in 62.16% of farms. • An artificial neural network was trained to optimize alternative source selection. [ABSTRACT FROM AUTHOR]

This article presents several insights from research of fire incident data from tank farms from around the world, which store products ranging from low flashpoint materials to crude and heavier hydrocarbons. The incidents and information in the database came from over 130 mostly publicly available sources, including technical and news reports. Updating of the database takes place when new studies require analysis of previous incidents. The database contained 595 fire incidents at the time of preparation of this article, and this research has already supported the analysis of the reliability of fire water protection system, hazard identification, and risk assessment for several tank farms. The research provided valuable insights regarding the frequency of tank farm fires, the frequency of boilovers, the frequency of injuries/fatalities, the expected numbers of injuries/fatalities per incident, the frequency of dike fires, the probability of failure of the fire water system, the time to extinguish (TtEx) the fires, the ignition source, and the types of tank roofs. When the database provided sufficient information, these numbers appear as a function of the material or product. The data also allowed plotting TtEx as a function of the (a) time for over a century, (b) tank diameter, and (c) number of tanks on fire. [ABSTRACT FROM AUTHOR]

The motivation of this paper stems from the growing concern of heavy pesticide use in developing countries such as Sri Lanka. Its excessive use affects the environment, the economy and farmers' health and yet farmers still seem unaware of its ill-effects. More worrying is the lack of appropriate government efforts to address this problem. If neglected, heavy use of pesticide eventually renders land useless, reduces future farm output and productivity and can severely affect farmers' health. This paper investigates pesticide use in agriculture and its impact on farm level technical efficiency using a metafrontier framework with stochastic frontier analysis. Data are collected through surveys of two groups of farmers in Sri Lanka. The first (described as the 'non-hospitalized farmers') are farmers who perceive that their ill-health is due to exposure to pesticides and obtained treatment but are not hospitalized. The second group (described as the 'hospitalized farmers') are farmers whose ill-health has been diagnosed by doctors and have been treated in hospital for exposure to pesticides. The results show that farmers overuse pesticides because of higher expectations of future returns which has led to an increase in inefficiency and remains unknown to the farmers. Determinants of inefficiency supports the argument that pesticide induced health problems reduce the health capital, while increasing farm level inefficiency. [ABSTRACT FROM AUTHOR]

Sentimental ties to land in Ireland result in less than 1 percent of agricultural land being sold each year and land rental is among the lowest of countries in the European Union. The findings highlight that longer leases are associated with large farms and those with a high percentage of land rented in, but revised policies are required to encourage more landowners to rent out their land. Improved land mobility can reduce issues such as barriers to entry for farmers, the economic vulnerability of farms, low farm succession rates, low pensions and an ageing farmer population. The history of land in Ireland and the non-pecuniary benefits of farming are also discussed. • The history of agricultural land in Ireland has resulted in strong sentimental and cultural ties to land. • Low land mobility in Ireland is evidenced by the fact that less than 1 percent of agricultural land is sold each year and Ireland has one of the lowest land rental rates in the European Union. • The findings highlight that longer leases are associated with large farms and those with a high percentage of land rented in. • Policies to increase long-term lease agreements have been somewhat successful. • However, there is little possibility of most farms being able to maximise the tax relief that is available. [ABSTRACT FROM AUTHOR]

A credible spatially explicit historical cropland dataset is the basis for accurately evaluating the impacts of anthropogenic land cover change on global changes. The credibility of the historical cropland datasets relies on both the reconstructed regional cropland area and the allocation algorithm. In the case of adopting the more reliable reconstructed historical cropland area in a given region, the credibility of the gridded cropland cover data mainly depends on the allocation algorithm, which makes a major contribution to the uncertainties of the spatial distribution of the cropland cover. In this study, a new land suitability-based gridded allocation algorithm for historical cropland cover was used to allocate the provincial cropland area into 5' × 5′ grids in China around 1850 C.E. The algorithm was constructed using the dominant physiogeographic factors that vary from region to region, and these factors were assessed by the modern cropland cover. The main conclusions are described as follows: (1) the cultivation intensity under a 5' × 5′ cell can be effectively indicated by the physiogeographic factors in a 0.5° × 0.5° unit, although the degrees of correlation showed a diverse variety with positive, negative correlation or irrelevance relationships in different regions. (2) The land suitability calculated from dominant regional physiogeographic factors could appropriately represent the cultivation intensity. The proportion of positive correlations above 0.7 between the modern cropland fraction and land suitability in China is more than 93%. (3) Employing the newly developed allocation algorithm, the 5' × 5′ cropland fraction data in China around 1850 C.E. was generated from the reconstructed provincial cropland area. The traditional farming regions were maturely cultivated in 1850 C.E., while the distribution of cropland was relatively small and had a lower cultivation intensity in the frontier region (Xinjiang Province and Northeast China, etc.). (4) The new allocation algorithm is expected to be applicable in regions with only very coarse spatial resolution cropland reconstruction results, and the reasonability of gridded allocation results will be improved, especially in regions at the early cultivation stages with very low intensity in China. [ABSTRACT FROM AUTHOR]

Global historical land use scenarios, including HYDE3.2 and PJ, are widely used in global change research. However, their uncertainties have not been well evaluated. Based on the historical-documents-based Chinese historical cropland dataset (CHCD), we evaluated the uncertainties of the representative global historical land use scenarios in past-millennium cropland reconstruction in China in terms of the area and spatial pattern. The results show that apparent differences exist in cropland area at the national scale between HYDE3.2 or PJ and CHCD for the Song-Liao-Jin Era (AD 1000–1279) and the Qing dynasty to present (AD 1644–1999), with a maximum relative difference ratio (RDR) of about −35% and 112%, respectively. We detected similar differences at the provincial scale, except for Guizhou, Heilongjiang, Jilin, and Xinjiang provinces. Specifically, HYDE3.2 underestimates cropland areas of nine provinces, with an absolute difference of up to 5.2 × 104 km2, while overestimates cropland areas of ten provinces, with an absolute difference of up to 5.1 × 104 km2. As for the spatial pattern, the percentage of grids with an absolute value of RDR greater than 70% between HYDE3.2 and CHCD reach up to 54%–82%, mainly distributed in major agricultural areas, frontier areas, and geographical transition zones. In conclusion, neither HYDE3.2 nor PJ reflects China's cropland area and its past-millennium change accurately, resulting from the differences in data sources, area estimation, and spatial allocation methods. [ABSTRACT FROM AUTHOR]

Regional reconstructions of historical cropland change are essential to improve the accuracy of global datasets. In Shaanxi province, central China, frequently affected by rebellions and natural disasters over the past 300 years, the cropland area fluctuated. In this study, based on local gazetteers, statistics, and survey data, systematic steps are followed to reconstruct the cropland area change since 1650 in Shaanxi province. The result shows four stages of changes in the total cropland area: steady increase during 1650–1830 stimulated by policies and population growth, decrease during 1830–1937 affected by wars and natural disasters, rapid growth during 1937–the 1980s, and rapid decrease after the 1980s influenced by urbanization and the agricultural planting structure adjustment. The total cropland area increased from 2.2 × 104 km2 in 1650 to 3.99 × 104 km2 in 2016. The majority of the cropland concentrated on the Guanzhong region from 1650 to 2016. The cropland expanded from the center to the surroundings in the Guanzhong region, from northeastern to southwestern in northern Shaanxi, and from western to eastern in southern Shaanxi before the 1980s. Cropland area declined in all three regions after the 1980s. The comparisons show most cropland area data in the Qing dynasty from the existing regional studies are still highly dependent on the registered tax-paying cropland area. Without considering agricultural history, changes in the SAGE cropland in Shaanxi province over the past 300 years are irrational. Spatially, HYDE 3.2 dataset cannot show the "real" spatial distribution of the cropland before 1930, because too much cropland is allocated to northern and southern Shaanxi. [ABSTRACT FROM AUTHOR]

• SOS pathway is a major pathway triggered by Ca2+ under salt stress. • Silicon has a role in the improved functioning of the SOS pathway under salt stress. • Calcium signalling and ABA synthesis results in stomatal closure. • High cytosolic Ca2+ results in upregulating ROS production. Owing to the sessile nature of plants, they are at the mercy of environmental conditions and are often subject to severe abiotic stresses. Amongst these abiotic stresses, high salinity stress is one of the most common and severe stresses affecting one-third of irrigated agricultural lands worldwide. Calcium ion is well known for its essential role as a mediator of signals and in regulating and specifying the cellular responses to abiotic stresses. To adapt to salt stress, one of the main strategies developed by plants is calcium signalling responses. The excess extracellular salt is detected by specific root receptors and resulted in immediate rise of cytosolic free Ca2+. Exhaustive research has been conducted to identify the genetic identities of Ca2+channels and signalling factors, developing novel findings on such tolerance mechanisms in plants. However, the complex interconnected system of the salinity stress response by Ca2+signalling in plants has not been adequately justified. Plants employ a combination of Ca2+ integrated proteins, phytohormones, osmolytes, receptors and other signalling factors in response to salinity stress, making this a complex calcium response network. In this review, we summarise and discuss the role of calcium signalling in response to salinity stress. In contrast to other studies, we try to provide a holistic overview of both chemical and mechanical processes involved in salt stress response while expanding on gaps filled by recent research. Furthermore, based on existing knowledge, we evaluate the different types of mechanisms and pathways through which calcium signalling works. [ABSTRACT FROM AUTHOR]

Ever-increasing unpredictability has led to recognition of increasing importance of innovation capabilities of businesses. In spite of recognizing such an important issue of innovation capability, not much research has been conducted on the relationship between innovation capabilities in business planning, R&D, commercialization and innovation performance. The current research, thus, intends to provide an empirical analysis of the effect of smart farm companies' innovation activities on their innovation performance. Classifying innovation capabilities into three categories of planning, R&D and commercialization capability, the current research aims to identify the effect of each category on sales and patent acquired. Moreover, it aims to identify the moderating effect of governmental policy and support for technology on the relationship between innovation activities and performance. It was found that planning, R&D and commercialization capabilities exerted a positive impact on business performance. It was also found that governmental policies and support helped enhance business performance. [ABSTRACT FROM AUTHOR]

Conservation Agriculture (CA) has got the huge potential to address the issue of global food security with ecological resilience and replenishment. The present study was conducted across 40 counties of the state of Ohio in the United States of America to assess and prioritize the social-ecological factors operating at CA farms, using both qualitative and quantitative methods. It has been elucidated from the study that selected owner-farmers of the study area (n = 230) have no hesitation to consider CA as the future of farming to address food security in response to mitigate climate change effects. They have been applying the combination of the CA principles on their farms: 60% of the respondents do not till their field, 80% leave crop residues on the soil surface, and 97% follow crop rotations. Finally, the CA farmers have achieved 10 to > 40% fuel savings during farm operations, having farm soil salinity reduced in the case of 70% of respondents. Apart from this, grazing animals are also integrated to CA systems, adding animal manure to their farms. However, the study indicates that there is a need to encourage the use of CA, which can minimize the negative impacts of dry spells in order to maximize crop production, and polish the strategies and policies in each and every sphere. • Successful adoption of conservation agriculture (CA) depends on the interaction between farm social-ecological factors. • Farmer's own perception drives adoption of conservation agriculture. • CA farmers are being rewarded with 10 to > 40% fuel savings during farm operations. • farm soil salinity reduced in the case of 70% of respondents who are practising CA. • Grazing animals are important to integrate within CA systems. [ABSTRACT FROM AUTHOR]

Despite being mostly dependent on agriculture, farm households in Bangladesh frequently engage in non-farm income diversification activities in order to maintain or increase their standard of living. The aim of this study is to examine the factors that influence farm households' strategies and decisions regarding the accumulation of non-farm income. Multi-stage sampling was used to collect primary data from 139 farm households in the Rajshahi district in order to meet the study aim. Descriptive and econometric analyses were both used for data analysis. The descriptive data showed that around 80% of the respondents earned income outside of agriculture. The findings of the binary logit model demonstrate that family size, years of schooling of the household head, and lack of participation in local organizations have a substantial impact on respondents' decisions on diversification. The multinomial logit model examined how family size, local organization membership, and farm size affected factor diversification outside of farming. Furthermore, the household head's years of schooling, participation in local organizations, and distance to the closest product market were the main factors influencing their diversification strategy. Policy makers should provide incentives and training that further stimulates non-farm income generating activities of farm households to facilitate the continuing transition of the Bangladesh economy away from its primary reliance on agriculture to generate national income. [ABSTRACT FROM AUTHOR]

We study smallholder households livelihood profiles in central Kenya in an area characterized by the presence of many large commercial farms. We surveyed 375 smallholder households, compared them according to three categories (employed, contract farmers, households non-engaged with commercial farms), and constructed a livelihood index. The results show that contract farmers and households employed on farms are only a small fraction of all smallholders. Employed and non-employed households show little difference in overall livelihood profiles. Results suggest that employment on large commercial farms is mainly a coping strategy for younger households or in times of need. Contract farmers were found only in a specific location and had better access to irrigation water and higher livestock holdings. Comparison with earlier data shows the persistence of precarious livelihood levels and household strategies aiming at diversification of activities, with little evolution over the last 20 years despite the presence of commercial farms. Overall, there is little evidence that the proximity to the commercial farms offers a way out of poverty for nearby smallholder farmers. • Employment on large farms is mainly a coping strategy during times of need. • Livelihood assets of employed households differed little from those not employed. • Contract farmers were found to be a minority and had more productive assets. • Longitudinal comparison shows the persistence of precarious livelihood levels. [ABSTRACT FROM AUTHOR]

Intensified human activities can augment soil organic carbon (SOC) losses from the world's croplands, making SOC a highly dynamic parameter both in space and time. Sentinel-2 spectral imagery is well placed to capture the spatiotemporal variability of SOC, but its capability has only been demonstrated for agricultural regions mostly located in Europe. Furthermore, most studies so far only used single-date images that resulted in spatially non-continuous SOC maps, hampering their ability to aid multiscale SOC assessments. Here, we aim to achieve spatially continuous mapping of SOC in croplands, by creating multitemporal bare soil composites that maximize cropland coverage in two regions of varying agroecosystems and landscape structure in the Northeast China Chernozem region and the Belgian Loam Belt. Bare soil pixels were extracted via spectral index thresholding that excluded contaminated pixels from external perturbance. Multitemporal soil composites were then obtained by averaging over multiple single-date bare soil images that were selected within pre-determined optimal time-windows, corresponding to the region-specific crop sowing periods when best possible surface conditions were expected. Results show that the optimal time-window filter ensured selective inclusion of single-date images that themselves yielded stable and robust SOC predictions across multiple years. Spectral-based models developed from multitemporal composites consistently produced better or similar prediction accuracies than single-date images for both study regions (R2: 0.52–0.62; RMSE: 0.17–0.21 g 100 g−1), while also achieved maximum cropland coverage (>82 %). Bootstrap modelling demonstrated that SOC mapping via multitemporal Sentinel-2 data was associated with small uncertainties. Investigations into the significant spectral bands that contributed to the prediction of SOC suggested that, regardless of the study regions, the physical relationship between spectral bands and SOC that predominantly exists for laboratory spectra is largely translated into Sentinel-2 platforms. This study highlights the widespread applicability of multitemporal Sentinel-2 remote sensing for effective and high-resolution SOC mapping, in order to detect localized soil degradation as well as to inform regional cropland management in diverse agroecosystems. [ABSTRACT FROM AUTHOR]

Agricultural commercialization and livelihood diversification have been proposed as ways to bring economic prosperity to rural zones after long-term violent conflict. Critics, however, argue that these market-based interventions exacerbate, rather than resolve, older social divisions, and that commercialization needs to be seen as part of agrarian transition processes. This paper contributes to the analysis of livelihoods-based interventions under violent conflict by presenting research from Kachin State, Myanmar. Drawing on 276 household surveys plus interviews, the paper argues that agrarian transition has only occurred within larger landholders who have been able to increase farm size by expanding commercial agriculture onto land historically used for shifting cultivation. Smallholders, however, have been unable to expand agriculture in this way, partly because of the reallocation of agricultural land to favored investors, including Chinese banana plantations. Meanwhile, access to non-agricultural livelihoods is largely restricted to laboring in Burmese army-controlled jade mines, or to traders arriving from outside the region. These findings indicate a different outcome to research elsewhere in Myanmar that suggests agrarian transition processes can benefit landless people; and instead supports evidence elsewhere in Asia that the agrarian transition can become "truncated" if smallholders do not participate. Making the agrarian transition inclusive requires greater attention to the ethnic, and other social barriers for participation by smallholders and rural landless, rather than facilitating commercialization alone. • Agrarian transition processes replicate rather than resolve violent conflict. • The agrarian transition in Kachin benefits large landholders and external traders. • Farm size increases by expanding agriculture on historic shifting cultivation land. • "Land grabs" prevent smallholders from expanding production. • Ethnic politics influence land tenure reforms and commercialization. [ABSTRACT FROM AUTHOR]

Farmers are increasingly required to record the actions taken and incidents occurring at farms. They are also expected to use the records to prove that animal welfare and environmental regulations are being followed. Yet, research suggests that record keeping disturbs, rather than supports, farming practices. The policy benefits are also suspected to be scarce. In this paper, we argue that the potential of records to act as useful calculative resources has been overlooked. The Finnish case study shows that recording nutrient use can allow fertilisation practices to be guided by water protection concerns. However, on some farms the requirement to keep nutrient records has been responded in ways that draws a distance between farms and regulatory control. The alignment of farm life and the recording requirements has followed different trajectories from one farm to another. Some farmers contest the science behind the fertilisation limits. Farms may also be poorly equipped to live with the limits that the records make visible. Optimal fertilisation then ceases to be an issue that can be solved by additional calculative support. Instead, record keeping indicates political divisions. • Farmers are required to record actions taken and incidents occurring at farms. • Research suggests that record-keeping disturbs farming practices. • Potentials of records to act as useful calculative resources have been overlooked. • The Finnish case-study shows that record-keeping may indicate political divisions. • On farms, record-keeping gains both mediating and distancing capacities. [ABSTRACT FROM AUTHOR]

Canada can radically reduce greenhouse gas (GHG) emissions by aggressively deploying agrivoltaics and reach its goal of cutting emissions by increasing the non-emitting share of electricity generation to 90 % by 2030. To help reach this goal, this study evaluated the potential energy production for vertical bi-facial solar photovoltaic arrays as well as the solar irradiation reaching the ground with three different spacings (5 m, 15 m and 45 m) and three different Canadian farming locations (London, Calgary and Winnipeg) using irradiance modeling with Ladybug tools plug-ins for Grasshopper and Honeybee. The crops currently grown in each region were identified and their sunlight requirements were analyzed. Based on the amount of solar radiation reaching the ground surface and the solar requirements of the crops, inter-row spacings that were suitable for agrivoltaic applications for the three locations were identified. Next the land acreage of a select few crops, which were proven to be satisfactory for agrivoltaic systems, were identified for each province and their electrical energy potential was ascertained using the open-source System Advisor Model. The results indicate that more than 84 % of the total national electricity requirements can be met by employing agrivoltaics on agricultural land where these crops are cultivated in the three provinces. • Agrivoltiac energy production for vertical bi-facial PV and solar irradiation reaching the ground. • Spacings (5 m, 15 m and 45 m) and Canadian farming locations (London, Calgary and Winnipeg). • Irradiance modeling with Ladybug tools plug-ins for Grasshopper and Honeybee. • Crops currently grown in each region identified and their sunlight requirements were analyzed. • Suitable agrivoltaics + land acreage of select crops quantified = 84 % Canada's electricity needs. [ABSTRACT FROM AUTHOR]

[Display omitted] • Fe 3 O 4 @LH was synthesized successfully via a one-pot synthetic strategy. • Application of Fe 3 O 4 @LH reduced soil total Pb by 16.7 %∼25.4 %. • Fe 3 O 4 @LH exhibited a maximum Pb adsorption capacity of 174.2 mg/g. • Adsorption process involved electrostatic adsorption, ion exchange, precipitation, and complexation. • Fe 3 O 4 @LH recovery exceeded 88.3% after a 90-day application period. Lead (Pb) contamination in agricultural soils poses a significant threat to both ecosystems and human health. While nano-Fe 3 O 4 exhibits promising potential for Pb remediation, its practical application in the soil is hindered by its' biotoxicity, easy aggregation, and the risk of secondary pollution. Thus, this study presents a novel approach wherein Fe 3 O 4 was incorporated into hydrogel via a one-pot synthetic strategy (Fe 3 O 4 @LH). This incorporation enhanced the mechanical properties and environmental stability of the hydrogel composites. Based on the mechanical properties, environmental stability, and single-point adsorption results for Pb, we selected Fe 3 O 4 @LH-4 for further research. The removal mechanism and the feasibility of employing Fe 3 O 4 @LH-4 for Pb removal from paddy soil were investigated through batch adsorption experiments and soil culture studies. Results showed that the adsorption process was primarily governed by swelling adsorption, electrostatic adsorption, ion exchange, precipitation, nanometer effect, and complexation mechanisms. The application of Fe 3 O 4 @LH-4 significantly led to the reduction of 16.7 %–25.4 % in soil Pb content, with removal rates escalating alongside increased dosage and application periods of Fe 3 O 4 @LH-4. Fitting results of the prediction model indicated that the Pb content in mildly Pb-contaminated soil (186.55 mg/kg) would decrease to be below the risk control standard for soil contamination of agricultural land in China (140 mg/kg) after 112 days of continuous application. Concurrently, cadmium and arsenic contents in the soil decreased by 5.2 %–10.8 % and 7.1 %–16.7 %, respectively. Moreover, the application of Fe 3 O 4 @LH-4 positively influenced soil nutrient levels, with total nitrogen and soil organic matter content significant increments of 13.6 %–41.0 % and 4.6 %–16.1 %, respectively. Furthermore, Fe 3 O 4 @LH-4 recovery exceeded 88.3 % after a 90-day application period. These findings underscore the potential of Fe 3 O 4 incorporated hydrogel as a promising agent for the sustained removal of heavy metal Pb from paddy soil. [ABSTRACT FROM AUTHOR]

New particle formation (NPF) significantly contributes to the number-based aerosol population in the ambient atmosphere, thereby affecting cloud formation and human health. We compared NPF characteristics, including the occurrence frequency, occurrence criteria, particle formation rate (J), and growth rate (GR) obtained from continuous measurements of the number–size distribution of particles and various gases, among different atmospheric environments (urban, agricultural (livestock and cropland), and Arctic sites). The highest NPF occurrence frequency (55%) was observed at agricultural (livestock) site (vs. urban, 45%; agricultural (cropland), 26%; Arctic, 24%). Despite the increased evaporation rate of nucleating clusters in summer, the NH 3 -rich environment (79.6 ppb) of pig and chicken farms promoted NPF. The NPF frequency at the Arctic showed a distinct seasonal pattern (high in summer and low in winter) when compared with that at the urban site (high in spring and fall and low in summer and winter). The J varied more significantly than GR among different sites, suggesting that J is more sensitive to the environment than GR. The highest GR (7.94 nm h−1) and J (2.72 cm−3 s−1) were observed at the agricultural (livestock) site, whereas the Arctic site had the lowest GR (3.34 nm h−1) and J (0.04 cm−3 s−1). The contribution of H 2 SO 4 vapor to NPF was the least in the Arctic and the highest in the urban environment. For NPF to occur, the ratio of the loss rate of condensable vapors to preexisting aerosols to their growth rate should be low, and this ratio was substantially lower in the Arctic than in the other environments. Our results underscore the complexity of NPF characteristics in different environments and highlight the interplay of various factors in governing NPF. • We compared new particle formation (NPF) characteristics at urban, agricultural, and Arctic sites. • The NPF frequency was the highest at the agricultural (livestock) site in summer. • In the Arctic, the NPF frequency showed a distinct seasonal pattern. • The L Γ threshold value, a criterion for NPF, was lower for the Arctic sites than for other sites. • Thus, NPF dynamics are complex in various environments and governed by several factors. [ABSTRACT FROM AUTHOR]

Organophosphorus pesticides (OPs), such as chlorpyrifos (CPF), are the most commonly used pesticides worldwide. Considering that OPs will eventually enter aquatic ecosystems due to runoff from agricultural lands, accidental leakage, and other unforeseen emergencies, monitoring water pollution of those substances is crucial for environmental protection and public health. In this study, Japanese medaka (Oryzias latipes) were exposed to CPF (0.03, 0.06, and 0.12 mg/L) for 6 h, and the time-series variations in their locomotor behavior and vocal traits were investigated. Compared with that measured before exposure, significantly changed locomotor behavior and vocal traits in Japanese medaka exposed to CPF could be observed at 4 h after exposure and thereafter, and the pattern of behavioral changes depends on the CPF concentrations. Exposure to CPF also changed the frequency-sound pressure level curve of Japanese medaka at 6 h after exposure, especially at 0.12 mg/L. Moreover, CPF exposure could significantly inhibit the acetylcholinesterase (AChE) activity in the brains and eyes of medaka, which exhibited significant correlations with the variation of locomotor behavioral and vocal traits. Considering that inhibiting the AChE activity is the primary mechanism underlying the neurobehavioral toxicity of all OPs, our finding suggested that simultaneously monitoring changes in the locomotor behavioral and vocal traits has a high potential to reflect the pollution of organophosphorus substances. [Display omitted] • CPF at 0.06 and 0.12 mg/L decreased motor activity and anxiety of Japanese medaka • CPF at 0.03 and 0.06 mg/L increased the pulse number of sound produced by medaka • CPF at 0.12 mg/L decreased pulse number and altered the acoustic spectrum of sound • CPF (0.03–0.12 mg/L) inhibited the AchE activity in fish brains and eyes within 6 h • Inhibited AchE activity associated with altered locomotor behavior and vocal traits [ABSTRACT FROM AUTHOR]

• The number of cabins in the Norwegian mountains is increasing since long. • Many cabins, trails and roads are found within good quality grazing areas. • Our findings raise concerns about cabin development in important grazing areas. • Conflicts may arise between food production and recreational homes. • Also in the mountains, all land-use interests must be considered in area planning. Mountain areas in Norway provide important resources for livestock grazing. These resources are crucial for agricultural production in a country with limited agricultural land and a climate and topography that restrict production of feed and food. A key contributor in the harvest of these resources has been mountain summer farming and outfield grazing in general. However, the use of mountainous grazing resources has been declining strongly for several decades with the regrowth of formerly open areas as a consequence. In contrast, recreational use, number of holiday cabins and associated infrastructure is rapidly increasing. Conflicts between recreational and agriculture use have received increasing attention in different media. We investigated the spatial patterns of cabin development and key grazing areas in Norwegian mountain areas, analysing data on livestock, cabins, and associated infrastructure. We found a large number of cabins and associated infrastructure within high-quality grazing areas indicating that the quality of grazing has not been adequately considered in the location of new cabins. Taking into consideration that cabin development seems not to decrease, the reduced availability of grazing resources may result in an increasing level of conflict and also impact food security in the long run. [ABSTRACT FROM AUTHOR]