Site-specific Application Research Articles

Development and Characterization of Ranitidine Hydrochloride Mucoadhesive Hydrogel Beads

Objectives: To formulate mucoadhesive hydrogel beads of Ranitidine hydrochloride for site-specific drug delivery in the stomach. The site-specific delivery was acquired by using mucoadhesive polymers in formulation development. Methods: The best method used for the preparation of mucoadhesive hydrogel beads was the ionic gelation method. For the achievement of mucoadhesive and controlled release properties of the formulation ionic gelation method used by varying concentrations of polymers. Findings: Prefomulation parameters were studied before the development of formulation such as melting point, and FTIR studies. Post-formulation evaluation parameters studied were drug content, invitro mucoadhesion test, swelling index, water uptake studies, SEM analytical studies, and invitro drug release studies. Nine formulations showed good swelling index, water uptake studies, and drug content. The SEM results revealed that beads maintained good surface morphology with a spherical shape. Mucoadherence was in the range of 66% to 77%. The optimized formulation was found with drug release up to 14hrs with 98.9% of drug release. Novelty: Mucoadhesive hydrogel beads of Ranitidine hydrochloride were developed by using primary polymer sodium alginate to acquire mucoadhesive properties and site-specific application. Other secondary polymers such as sodium carboxy methylcellulose, hydroxy propyl methyl cellulose, and Carbopol contribute to the controlled release of drugs from hydrogel beads. Keywords: Hydrogel beads, Ranitidine HCl, Sodium alginate, Ionotropic gelation, Mucoadhesion

Assessing the Effectiveness of Satellite and UAV-Based Remote Sensing for Delineating Alfalfa Management Zones Under Heterogeneous Rootzone Soil Salinity

Site-specific application of agricultural inputs is crucial for optimizing resource utilization in alfalfa (Medicago sativa L.) production and addressing challenges such as soil salinity. The main objective of this study was to assess the effectiveness of PlanetScope and UAV-based NDVI imagery for delineating alfalfa management zones under heterogeneous rootzone soil salinity. The research was conducted in the alfalfa field located in Imperial Valley, CA. The extent of rootzone soil salinity was assessed using Electromagnetic induction (EMI) technology and deep soil sampling. Reference management zones were then defined using the soil salinity (ECe) map derived from apparent electrical conductivity (ECa) data. Additionally, a time series of NDVI images from PlanetScope imagery and an NDVI image captured using an unmanned aerial vehicle were used to delineate remote sensing-based management zones. Laboratory analysis of disturbed soil samples collected at various depths provided soil physicochemical property data. Soil salinity of the samples ranged from 2.2 to 13.4 dS m−1 with a moderate level of variability (CV = 37.7%). ECe-based management zones accounted for approximately 83% of the field's variability and exhibited substantial differentiation among delineated zones concerning diverse soil properties, including ECa, ECe, gravimetric water content, Mg2+, boron, Ca2+, Na+, and Cl−. Notably, NDVI images effectively captured field variability on par with ECe-based zoning. Moreover, NDVI images recommended the same optimal number of zones (i.e., three) to address the field's variability, aligning with the ECe-based zoning approach. Our findings highlight that heterogeneity of soil salinity in the root zone primarily impacts the variability of alfalfa NDVI early in the growing season. Consequently, this early stage emerges as the most opportune timeframe for NDVI-based zoning for rapid assessment of rootzone soil salinity concerns.

The reduction of chemical inputs by ultra-precise smart spot sprayer technology maximizes crop potential by lowering phytotoxicity

Weeding is at the heart of agriculture. Today, using herbicides is unavoidable in conventional agriculture. However, increasing ecological awareness, the need to reduce carbon dioxide emissions, and the pursuit of sustainable food have driven industries and academics to explore new approaches for growing and protecting crops. In this context, smart spot sprayers emerge as a highly innovative alternative to broadcast treatments, which overuse pesticides, and mechanical weeding, which is limited by restrictive application conditions. Using artificial intelligence (AI) for plant recognition and ultra-precise spraying technology, herbicides are applied directly to weeds with minimal overspray, reducing their use by 90% compared to conventional methods. Spot sprayers' narrowly targeted spray limits chemicals' contact with the crop, resulting in low phytotoxicity, preserving natural crop development while maximizing yield potential. In addition, site-specific application increases the selectivity of chemical products artificially, enabling more concentrated mixtures to be used. Also, AI enables the selective application of non-selective molecules, supporting their use in established cultures. These characteristics open new opportunities for effective weed control where the traditional solutions fall short. This publication demonstrates the potential of the ultra-precise smart spot sprayer ARA in weed management in onion and sugar beet crops. Although comparing the overall performance of ARA with other smart spot sprayers on the market cannot be done in this article, this technology's potential in rendering weed management on farms more effective, sustainable, and saving labor without compromising yield is nevertheless indicated.

Impact of Weather Parameters and Application Methods of Micronutrients on Dry Matter Production and Yield of Chickpea Under the Riverine Area of Bihar Region of India

ABSTRACT Weather and climate uncertainties significantly impact crop and food production, particularly in the Bihar region of India. Crop cultivation in this region faces significant risks due to several climatic challenges: rising minimum temperatures in the winter (rabi), extreme temperatures in the spring, intense rainfall, and extended dry spells during the kharif ;(monsoon) season. The growth and development of the plants majorly depend on the climatic and nutrient factors. The plant uptake and utilization of nutrients are surprisingly reliant on the weather conditions. Therefore, understanding the interaction of climatic factors and nutrient application methods is crucial for achieving higher growth and yield stewardship. A two-year field experiment was conducted from 2019 to 2021 in the riverine area (Diara) of Bihar, India, to evaluate the relationship between weather parameters and application methods of Molybdenum and Boron on the dry matter production (DMP) and grain yield of chickpea. Six different application methods of Boron and Molybdenum were tested. The study aimed to identify the ideal micronutrient application methods for chickpea production in the rabi season under the Diara ;(riverine) area. Results indicated that the combination of boron basal + molybdenum seed treatment produced the highest grain yield (1740 kg ha−1) and dry matter production (5.81 g plant−1) at all phenological phases, performing on par with boron basal + molybdenum foliar application for both years. Concerning the weather parameters, the correlation studies showed that all the direct weather parameters and agrometeorological indices showed positive and negative effects (p = .05) on DMP and grain yield of chickpea during both years. This study contributes to the development of site-specific application methods for molybdenum and boron, identifies optimal sowing times, minimizes the negative impacts of climate change, and maximizes chickpea production in the riverine (Diara) area of Bihar.

Stimulating awareness of Precision Farming through gamification: The Farming Simulator case

Precision Farming (PF) provides different solutions to assist the decision-making process on farms. Current PF technologies such as variable rate site-specific applications can bring financial benefits to farmers as well as environmental advantages. Increasing scientific research and an expanding number of PF products are supporting a growing interest in PF applications. However, the actual implementation of these technologies on farms in many cases remains low. Therefore, there is a need to disseminate and transfer knowledge about the positive aspects of PF. One of the ways to facilitate the adoption process of PF technologies is education and training among farmers and other interested stakeholders. This paper presents a case study using the computer game Farming Simulator as an educational tool for raising awareness about the topic in an engaging and enjoyable way. Two distinct downloadable content (DLC) versions were developed and implemented in the versions 2019 and 2022 of the game, respectively, each with a range of PF functionalities (automatic steering, variable rate applications, yield mapping among others). The PF DLCs have received positive feedback from students and scientists but also the general public. The growing number of downloads (3,661,069 in total for both DLC versions as of 15th November 2023) demonstrates the effectiveness of computer games as an educational tool to educate and inform stakeholders (farmers, scientists, students, and the general public) about agricultural challenges and the potential of PF as a solution.

Economic and Environmental Assessment of Variable Rate Nitrogen Application in Potato by Fusion of Online Visible and Near Infrared (Vis-NIR) and Remote Sensing Data

Addressing within-field spatial variability for nitrogen (N) management to avoid over and under-use of nitrogen is crucial for optimizing crop productivity and ensuring environmental sustainability. In this study, we investigated the economic, environmental, and agronomic benefits of variable rate nitrogen application in potato (Solanum tuberosum L.). An online visible and near-infrared (vis-NIR) spectroscopy sensor was utilized to predict soil moisture content (MC), pH, total organic carbon (TOC), extractable phosphorus (P), potassium (K), magnesium (Mg), and cation exchange capacity (CEC) using a partial least squares regression (PLSR) models. The crop’s normalized difference vegetation index (NDVI) from Sentinel-2 satellite images was incorporated into online measured soil data to derive fertility management zones (MZs) maps after homogenous raster and clustering analyses. The MZs maps were categorized into high fertile (VR-H), medium–high fertile (VR-MH), medium–low fertile (VR-ML), and low fertile (VR-L) zones. A parallel strip experiment compared variable rate nitrogen (VR-N) with uniform rate (UR) treatments, adjusting nitrogen levels based on fertility zones as 50% less for VR-H, 25% less for VR-MH, 25% more for VR-ML, and 50% more for VR-L zones compared to the UR treatment. The results showed that the VR-H zone received a 50% reduction in N fertilizer input and demonstrated a significantly higher crop yield compared to the UR treatment. This implies a potential reduction in negative environmental impact by lowering fertilizer costs while maintaining robust crop yields. In total, the VR-N treatment received an additional 1.2 Kg/ha of nitrogen input, resulting in a crop yield increase of 1.89 tons/ha. The relative gross margin for the VR-N treatment compared to the UR treatment is 374.83 EUR/ha, indicating substantial profitability for the farmer. To further optimize environmental benefits and profitability, additional research is needed to explore site-specific applications of all farm resources through precision agricultural technologies.

Delay and Energy Efficient Offloading Strategies for an IoT Integrated Water Distribution System in Smart Cities

In the fast-moving world of information and communications technologies, one significant issue in metropolitan cities is water scarcity and the need for an intelligent water distribution system for sustainable water management. An IoT-based monitoring system can improve water distribution system management and mitigate challenges in the distribution network networks such as leakage, breakage, theft, overflow, dry running of pumps and so on. However, the increase in the number of communication and sensing devices within smart cities has evoked challenges to existing communication networks due to the increase in delay and energy consumption within the network. The work presents different strategies for efficient delay and energy offloading in IoT-integrated water distribution systems in smart cities. Different IoT-enabled communication network topology diagrams are proposed, considering the different water network design parameters, land cover patterns and wireless channels for communication. From these topologies and by considering all the relevant communication parameters, the optimum communication network architecture to continuously monitor a water distribution network in a metropolitan city in India is identified. As a case study, an IoT design and analysis model is studied for a secondary metropolitan city in India. The selected study area is in Kochi, India. Based on the site-specific model and land use and land cover pattern, delay and energy modeling of the IoT-based water distribution system is discussed. Algorithms for node categorisation and edge-to-fog allocation are discussed, and numerical analyses of delay and energy models are included. An approximation of the delay and energy of the network is calculated using these models. On the basis of these study results, and state transition diagrams, the optimum placement of fog nodes linked with edge nodes and a cloud server could be carried out. Also, by considering different scenarios, up to a 40% improvement in energy efficiency can be achieved by incorporating a greater number of states in the state transition diagram. These strategies could be utilized in implementing delay and energy-efficient IoT-enabled communication networks for site-specific applications.

Towards site-specific management of soil organic carbon: Comparing support vector machine and ordinary kriging approaches based on pedo-geomorphometric factors

Site-specific management of soil organic carbon (SOC) is crucial for cleaner, sustainable agricultural production and climate change mitigation. The Neyshabur plain of northeastern Iran is plagued with spatially variable critically low SOC. Thus, in this study management zones (MZs) were developed for the site-specific management of SOC. Soil samples (0–0.3 m) and digital elevation model-derived geomorphometric variables were collected at 288 locations. Soil pH, clay content, available phosphorus, available potassium, and the vertical distance to channel network significantly (p < 0.05) correlated with SOC. Subsequently, support vector machine (SVM) and ordinary kriging (OK) approaches were used to map the spatial variability and develop MZs based on SOC and its above-listed pedo-geomorphometric covariates. Generally, the coefficient of determination (R2) and root mean square error (RMSE) of OK and SVM models used to interpolate pedo-geomorphometric covariates, were similar. However, the Fuzzy Performance Index (FPI) and Normalized Classification Entropy (NCE) indices suggest that the MZs based on the SVM model (FPI = 0.091, NCE = 0.113) are more accurate compared to those determined using the OK (FPI = 0.135, NCE = 0.164) method. Although the results demonstrate the potential of both OK and SVM approaches for site-specific management of SOC in the Neyshabur plain, the average difference of 0.8 g kg−1 SOC in the SVM approach compared to OK (p < 0.05) was found to be significant, especially along transitional boundaries between MZs. Thus, the SVM-based MZs developed in this study can be used for more accurate site-specific application of organic amendments, mineral fertilizers, and management practices that improve carbon sequestration and sorption of chemical contaminants in the Neyshabur plain of Iran.

Smart sprayer a technology for site-specific herbicide application

Reducing the use of chemical-synthetic herbicides with site-specific application technology will play an important role in agricultural research and practice in the coming years and decades. This paper presents a sprayer for site-specific weed control in row crops using weed detection technology based on image analysis and spot application using weed detection data. Two small plot trials in sugar beet and maize in 2021 and seven On-farm field trials in sugar beet, sunflower and maize in 2022 are presented. A pair-wise comparison of broadcast application, untreated control and spotting application were realized. A zero-tolerance strategy for weeds was used as a spotting application in all experiments. Herbicide savings from 10% to 55% were realized without a significant decrease in efficacy in weed control for the spotting application compared to the broadcast application. The weed control efficacy for broadcast application varied from 70 to 100%, while for the spotting application 72–99%. Weed control between broadcast and spotting did not differ significantly within a trial, except one experiment. Application mistakes caused the partly observed weed control of under 80%. Yield tends to be higher for all spotting treatments compared to broadcast application. The presented system provides a robust spotting technology for effective weed management with lower environmental risk and a positive effect on biodiversity.

Enhanced Sea Horse Optimization with Deep Learning-based Multimodal Fusion Technique for Rice Plant Disease Segmentation and Classification

The detection of diseases in rice plants is an essential step in ensuring healthy crop growth and maximizing yields. A real-time and accurate plant disease detection technique can assist in the development of mitigation strategies to ensure food security on a large scale and economical rice crop protection. An accurate classification of rice plant diseases using DL and computer vision could create a foundation to achieve a site-specific application of agrochemicals. Image investigation tools are efficient for the early diagnosis of plant diseases and the continuous monitoring of plant health status. This article presents an Enhanced Sea Horse Optimization with Deep Learning-based Multimodal Fusion for Rice Plant Disease Detection and Classification (ESHODL-MFRPDC) technique. The proposed technique employed a DL-based fusion process with a hyperparameter tuning strategy to achieve an improved rice plant disease detection performance. The ESHODL-MFRPDC approach used Bilateral Filtering (BF)-based noise removal and contrast enhancement as a preprocessing step. Furthermore, Mayfly Optimization (MFO) with a Multi-Level Thresholding (MLT) based segmentation process was used to recognize the diseased portions in the leaf image. A fusion of three DL models was used for feature extraction, namely Residual Network (ResNet50), Xception, and NASNet. The Quasi-Recurrent Neural Network (QRNN) was used for the recognition of rice plant diseases, and its hyperparameters were set using the ESHO method. The performance of the ESHODL-MFRPDC method was validated using the rice leaf disease dataset from the UCI database. An extensive comparison study demonstrated the promising performance of the proposed method over others.

A 3D-Printed Assemblable Bespoke Scaffold as a Versatile Microcryogel Carrier for Site-Specific Regenerative Medicine.

Advances in additive manufacturing have led to diverse patient-specific implant designs utilizing computed tomography, but this requires intensive work and financial implications. Here, Digital Light Processing is used to fabricate a hive-structured assemblable bespoke scaffold (HIVE). HIVE can be manually assembled in any shape/size with ease, so a surgeon can create a scaffold that will best fit a defect before implantation. Simultaneously, it can have site-specific treatments by working as a carrier filled with microcryogels (MC) incorporating different biological factors in different pockets of HIVE. After characterization, possible site-specific applications are investigated by utilizing HIVE as a versatile carrier with incorporated treatments such as growth factors (GF), bioceramic, or cells. HIVE as a GF-carrier shows a controlled release of bone morphogenetic protein/vascular endothelial growth factor (BMP/VEGF) and induced osteogenesis/angiogenesis from human mesenchymal stem cells (hMSC)/human umbilical vein endothelial cells (HUVECs). Furthermore, as a bioceramic-carrier, HIVE demonstrates enhanced mineralization and osteogenesis, and as a HUVEC carrier, it upregulates both osteogenic and angiogenic gene expression of hMSCs. HIVE with different combinations of MCs yields a distinct local effect and successful cell migration is confirmed within assembled HIVEs. Finally, an in vivorat subcutaneous implantation demonstrates site-specific osteogenesis and angiogenesis.

Estimation of Sensor-based site specific variable rate fertilizer application for maize (Zea mays L.) crop

Optical spectrometry sensors in crops offer a remarkable technological breakthrough in the field of variable-rate nitrogen fertilization. A field study was conducted during rainy (kharif) season of 2021 at the research farm of the Agricultural Engineering College and Research Institute Tamil Nadu Agricultural University Coimbatore to estimate maize crop nitrogen (N), Normalized Difference Vegetation Index (NDVI) value and chlorophyll content in hybrid maize COH (M) 8. Fertilizers were administered to the plots following the recommendations (250:75:75 kg NPK ha-1) given under Soil Test Crop Response, with a goal yield of 9t ha-1 predicted based on the initial soil available N, P, and K values. The experimental findings revealed a significant impact of nitrogen rate (P&lt;0.001) on the percentage of nitrogen content in the leaves (% N leaf content). Additionally, there was a decrease in maize leaf chlorophyll content index over time, with ranges of 32.96 to 50.57, 28.78 to 41.78, 24.81 to 35.86, 22.12 to 28.54, and 14.34 to 20.56. On the contrary, the NDVI experienced an increase throughout the season, with ranges of 0.32 to 0.49, 0.30 to 0.55, 0.28 to 0.66, 0.46 to 0.88, and 0.56 to 0.84. The study will help foster sustainability within modern intensive farming practices by emphasizing the importance of reducing environmental pollution caused by applying Sensor-based site-specific nitrogen fertilizer for maize crop.

Effect of Site-Specific Application of N and K Fertilizers on Cashew Growth and Yield in Ochaja, Kogi State, Nigeria

An experiment was conducted at the Ochaja Substation of the Cocoa Research Institute in Ibadan, Nigeria. Ochaja is located in the guina savanna zone of Nigeria. Plantation soil nutrient requirements were determined prior to applying the required fertilizers. The soil was deficient in nitrogen (N) and potassium (K), with values ​​of 0.41 g/kg soil and 0.012 cmol/kg soil, respectively, at depths 0–40 cm. This value was used to calculate cashew fertilization rates of 54 kg N/ha and 84 kg K2O/ha. Based on these values, four treatment combinations of two rates of nitrogen fertilizer and two rates of potassium fertilizer were formed and applied to young cashew trees on the field. Nitrogen fertilizer was applied at 0 and 54 kg N/ha while the Potassium was applied at 0 and 84 kg K/ha. Treatments were placed in a Randomized Complete Block Design (RCBD) with three replications, Cashew growth, nut yield and soil nutrient characteristics were measured. The first dose was applied in June while the second dose was applied in September of 2019 and 2020. Application of N fertilizer had a significant lowering effect on soil pH at both soil depths compared to the control and compared to N and K fertilizers applied together. Similarly, application of potassium-based fertilizer, with or without nitrogen fertilizer, slightly increased soil pH by 5.4%. Soil N had a significant effect (P &lt; 0.05). Total N ranged from 0.5 g/kg to 0.7 g/kg soil for fertilizer treatments. N and K fertilizers did not significantly improve available soil phosphorus (P) across fertilization treatments. Exchangeable K in soil followed a similar trend with available P in different treatments. Yield of cashew nuts (280kg) improved significantly (P &lt; 0.05) under application of sole nitrogen fertilizer and the control.

Towards new ecologies of automation: Robotics and the re-engineering of nature

Climate/ecological breakdown and automation are two defining challenges of the current era, yet there is little research on their conjunctural intersection. Across experimental landscapes from agriculture to conservation to mining to weather modification, automation technologies are increasingly being presented as the key to fixing, managing and even transcending the turbulent ecologies of the Anthropocene which threaten social and economic reproduction. This emerging set of visions, experiments and uses rest on the systemic capabilities of bundled robotic and autonomous system technologies (e.g. advanced sensors machine vision, artificial intelligence, robotics) to see, know and intervene in the biophysical world in new ways. This, we argue, potentially represents a shift beyond logics of mitigation and adaptation towards engineering nature in the face of converging environmental threats. Synthesising insights from existing literature, we develop a conceptual framework for understanding the ‘new ecologies of automation’ and diverse, site-specific applications across what we call ‘operational ecologies’. We then explore a range of diverse exemplars, creating a typology of operational ecologies before discussing key logics, themes and directions for critical research. Overall, the paper makes a significant and original contribution to knowledge in critical geography, and the under-researched intersection between political ecology and automation studies.

Multiple coronally advanced flap with a selective use of connective tissue graft: A 3-year prospective clinical and histological study.

The purpose of the present study was to prospectively evaluate the 3-year changes in the gingival dimensions following multiple coronally advanced flap (MCAF) with selective use of connective tissue graft (CTG). In addition, the secondary aim was to histologically identify the factors related to phenotype changes. Twenty patients treated with MCAF and site-specific application of a CTG were available for the 3-year follow-up. Outcome measures included complete root coverage (CRC), recession reduction, keratinized tissue width (KTW), marginal tissue thickness changes, and primary flap position. Biopsies were harvested at one of the sites treated with the adjunct of CTG. All sections were stained with hematoxylin and eosin, Masson trichrome, Verhoeff-van Gieson, tenascin, and alcian blue stain for semiquantitative evaluation. At 3 years, CRC was detected in 86% of sites treated with MCAF alone and 81% of sites treated with MCAF+CTG. The 47% of sites treated with MCAF+CTG presented an apical shift of primary flap from its original position. Linear regression showed a significant association between KTW change and the initial KTW in MCAF-treated sites, while both initial KTW and position of primary flap were statistically significantly associated factors with KTW changes in the MCAF+CTG group. In all the biopsies examined, there is always a marked and clear separation between the connective tissue of the gingival flap and the palatal connective tissue of the graft. The selective use of CTG is an effective treatment for multiple gingival recessions. Only a limited increase in KTW can be expected in a bilaminar technique if, during the healing phases, the connective tissue is maintained completely covered.

Characterization and Mapping of Soil-Landscape for Site-Specific Soil Management in Ayiba Watershed, Northern Highlands of Ethiopia

The characterization of soil landscapes is becoming increasingly important for making decisions regarding site-specific agriculture systems and soil management. This study was initiated for the purpose of identifying landscape-scale spatial soil variation using a toposequence model so that site-specific fertilization could be achieved. According to the finding, the soils were shallow to very deep in depth, moderately acidic to moderately alkaline in soil reaction, nonsaline in salinity, and clay to sandy loam in texture. The soils were found very low to low levels in most soil nutrients, very low to very high levels of base saturation, and deficient in zinc but have adequate levels of iron, copper, and manganese. The soil exchange complex was mainly dominated by Ca and Mg where the order of occurrence was Ca &gt; mg &gt; K &gt; Na. The CEC values were in high to very high range. Following the field survey and soil analytical results, five main reference soil groups of the World Soil Resource Base—Leptosols (56%), Luvisols (8.5%), Fluvisols (14.4%), Vertisols (13%), and Cambisol (8.2%)—were identified and mapped. Leptosols cover the largest landmass of the watershed and mostly found at the summit and hill back slopes. On the other hand, Luvisols, Fluvisols, Vertisols, and Cambisols were found on the middle and foot slopes. According to the findings, the variation in soil source indicating that topography is the primary pedogenic element in the formation of the soil in the watershed that was under research. Therefore, having local-scale-specific soil information can assist the site-specific application of soil nutrients and amendments based on spatial variability which is tailored to the soil requirements.

Potential of Precise Fertilization through Adoption of Management Zones Strategy to Enhance Wheat Production

The variability in soil properties and crop yield can be overcome by adoption of smart farming practices through interpolation and mapping of spatial variability patterns. Geospatial technologies can be utilized to determine the cause of spatial variability in fields for site-specific application of fertilizer. This study was designed to quantify and identify the spatial variation in soil properties and wheat (Triticum aestivum L.) yield and to delineate prescription maps for precise application of fertilizer in a semi-arid subtropical region of Pakistan. To examine the variability in soil properties on the production of the considered crop, this study comprised two different fields and each field was divided into (20 × 20 m) grids. The samples of soil were collected at 15 cm and 30 cm soil depths before the fertilization to analyze the different soil characteristics i.e., nitrogen (N), electrical conductivity (EC), potassium (K), soil organic matter (SOM), phosphorus (P), and pH. The boundaries of selected fields and grid points were established with a real-time kinematics-global positioning system (RTK-GPS). The soil data were acquired with a soil proximal sensor at a depth of 7 cm after fertilization. The statistical analysis coefficient of variation (CV), geostatistical-analysis-nugget-to-sill ratio (N:S), and the interpolated maps (ArcGIS pro 2.3) were used to characterize the least to moderate variability of soil parameters and yield, demanding site-specific management of fertilizer application. Cluster analysis was conducted using Minitab 21, which classified soil and yield characteristics into five categories: “very good”, “very low”, “good”, “poor”, and “medium”, with an external heterogeneity and internal homogeneity both more than 60%. Significant relationships (p &lt; 0.05) between soil and crop properties were used to develop the management zones (MZs) for the precise application of fertilizer in wheat fields. Significant differences (p &lt; 0.05) in soil nutrients were found in the very high and very low productivity zones at both sampling times, which suggest delineating the MZs for precise application of fertilizer according to the need of crop and soil properties. The results revealed that the optimum number of MZs for the wheat fields was five and there was heterogeneity in the soil nutrients in five MZs. The findings of this study also highlight the necessity of predicting the crop and soil factors by using precision technologies to develop the prescription maps, because sampling and analysis of soil are expensive and time-consuming. Based on the demand of the soil and crops, site-specific fertilization can increase economic and environmental efficiency.

Enhancing the activity of a monomeric alcohol dehydrogenase for site-specific applications by site-directed mutagenesis.

Gene fusion or co-immobilization are key tools to optimize enzymatic reaction cascades by modulating catalytic features, stability and applicability. Achieving a defined spatial organization between biocatalysts by site-specific applications is complicated by the involvement of oligomeric enzymes. It can lead to activity losses due to disturbances of the quaternary structures and difficulties in stoichiometric control. Thus, a toolkit of active and robust monomeric enzymes is desirable for such applications. In this study, we engineered one of the rare examples of monomeric alcohol dehydrogenases for improved catalytic characteristics by site-directed mutagenesis. The enzyme from the hyperthermophilic archaeon Thermococcus kodakarensis naturally exhibits high thermostability and a broad substrate spectrum, but only low activity at moderate temperatures. The best enzyme variants showed an ~5-fold (2-heptanol) and 9-fold (3-heptanol) higher activity while preserving enantioselectivity and good thermodynamic stability. These variants also exhibited modified kinetic characteristics regarding regioselectivity, pH dependence and activation by NaCl.

Mobilization of subsurface carbon pools driven by permafrost thaw and reactivation of groundwater flow: a virtual experiment

Permafrost thaw leads to an increase in groundwater circulation and potential mobilization of organic carbon sequestered in deep Arctic sediments (e.g. 3–25 m below surface). Upon thaw, a portion of this carbon may be transported along new groundwater flow paths to surface waters or be microbially transformed or immobilized by in-situ biogeochemical reactions. The fate of thaw-mobilized carbon impacts surface water productivity and global climate. We developed a numerical model to investigate the effects of subsurface warming, permafrost thaw, and resultant increased groundwater flow on the mobilization and reactive transport of dissolved organic carbon (DOC). Synthetic simulations demonstrate that mobilization and groundwater-borne DOC export are determined by subsurface thermo-chemical conditions that control the interplay of DOC production (organic matter degradation), mineralization, and sorption. Results suggest that peak carbon mobilization from these depths precedes complete permafrost loss, occurring within two centuries of thaw initiation with the development of supra-permafrost groundwater flow systems. Additionally, this study highlights the lack of field data needed to constrain these new models and apply them in real-word site-specific applications, specifically the amount and spatial variability of organic carbon in deep sediments and data to constrain DOC production rates for groundwater systems in degrading permafrost. Modeling results point to key biogeochemical parameters related to organic matter and carbon bioavailability to be measured in the field to bridge the gap between models and observations. This study provides a foundation for further developing a physics-based modeling framework to incorporate the influence of groundwater flow and permafrost thaw on permafrost DOC dynamics and export, which is imperative for advancing understanding and prediction of carbon release and terrestrial-aquatic carbon exchange in warming Artic landscapes in the coming decades.

Continental-scale hyperspectral tree species classification in the United States National Ecological Observatory Network

Advances in remote sensing imagery and machine learning applications unlock the potential for developing algorithms for species classification at the level of individual tree crowns at unprecedented scales. However, most approaches to date focus on site-specific applications and a small number of taxonomic groups. Little is known about how well these approaches generalize across broader geographic areas and ecosystems. Leveraging field surveys and hyperspectral remote sensing data from the National Ecological Observatory Network (NEON), we developed a continental-extent model for tree species classification that can be applied to the network, including a wide range of US terrestrial ecosystems. We compared the performance of a model trained with data from 27 NEON sites to models trained with data from each individual site, evaluating advantages and challenges posed by training species classifiers at the US scale. We evaluated the effect of geographic location, topography, and ecological conditions on the accuracy and precision of species predictions (72 out of 77 species).On average, the general model resulted in good overall classification accuracy (micro-F1 score), with better accuracy than site-specific classifiers (average individual tree level accuracy of 0.77 for the general model and 0.70 for site-specific models). Aggregating species to the genus-level increased accuracy to 0.83. Regions with more species exhibited lower classification accuracy. Predicted species were more likely to be confused with congeneric and co-occurring species and confusion was highest for trees with structural damage and in complex closed-canopy forests. The model produced accurate estimates of uncertainty, correctly identifying trees where confusion was likely. Using only data from NEON, this single integrated classifier can make predictions for 20% of all tree species found in forest ecosystems across the entire US, which make up to roughly 90% of the upper canopy of the studied ecosystems. This suggests the potential for integrating information from multiple datasets and locations to develop broad scale general models for species classification from hyperspectral imaging.