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Nitrogen, as one of the important elements affecting the growth and development of fruit trees, leads to slowed protein synthesis and reduced photosynthesis, resulting in yellowing of the leaves, poor tree growth, and decreased yield under nitrogen-deficient conditions. In order to minimize losses and maximize fruit yield, there is often an occurrence of excessive fertilization, soil structure degradation, and water pollution. Therefore, accurate and real-time monitoring of nitrogen content in fruit trees has become the fundamental prerequisite for precision management of orchards. Furthermore, precision orchard management is crucial for enhancing fruit quality by maintaining the optimal growth conditions necessary for trees. Moreover, it plays a vital role in safeguarding the ecological environment by mitigating the overuse of fertilizers and pesticides. With the continuous development and application of spectral remote sensing technology in agricultural monitoring and land management, this technology can provide an effective method for monitoring nitrogen content. Based on a review of relevant literature, this paper summarizes a research framework for monitoring and inversion of nitrogen content in fruit trees, which provides help for further research. Firstly, based on different remote sensing platforms, the application was discussed, on spectral remote sensing technology in the acquisition of nitrogen content in fruit trees. Secondly, the index parameters that can reflect the nitrogen content of fruit trees are summarized, which provides practical guidance for remote sensing monitoring. Additionally, the regression algorithms and application situations based on spectral data for nitrogen content were introduced. In conclusion, in response to the current issues and technological limitations, future research should focus on studying the nitrogen content characteristics of fruit trees during different phenological periods, integrating multi-type data information, and thereby improving the universality of the nitrogen content inversion model for fruit trees. [ABSTRACT FROM AUTHOR]

Blueberry growers currently rely on manual or subjective approaches to harvest decision-making and yield estimation. Canopy image-based, pre-harvest blueberry detection provides a promising means for automated harvest maturity assessment, fruit counting, and yield estimation to optimize crop management and reduce labor costs. Data-driven deep-learning-based object detectors, especially the YOLO (You Only Look Once) series models, have emerged as a powerful tool for a diversity of computer vision tasks in agriculture. However, the detection of such small, densely populated objects as blueberries in unstructured scenes can pose great challenges, especially given the lack of dedicated datasets for model development. This study, therefore, presents the first publicly available dataset of blueberry canopy images with 17,955 ripe and unripe blueberries, which were captured in diverse orchard conditions, and performance evaluation of YOLOv8l (large) and YOLOv9-c (compact) with comparable complexity for blueberry detection and whereby fruit counting and ripe fruit percentage estimation. At the input image resolution of 2560 × 2560 pixels, both models performed similarly in terms of detection accuracy with around 91 % mAP@50, except that YOLOv9-c was far more time-consuming to train. Trained with the input of higher resolution images of 3520 × 3520 pixels, YOLOv8l achieved an overall mAP@50 of nearly 93 %, and an RMSEs (root-mean-square errors) of 10.4 in fruit counting and 3.62 % in estimating the percentage of ripe fruit of each image. Both the blueberry dataset11 https://zenodo.org/records/14002517 and software programs22 https://github.com/vicdxxx/BlueberryDetectionAndCounting of this study have been or will be made publicly available, which are expected to be useful for promoting greater community efforts for further algorithm development.

Nitrogen, as one of the important elements affecting the growth and development of fruit trees, leads to slowed protein synthesis and reduced photosynthesis, resulting in yellowing of the leaves, poor tree growth, and decreased yield under nitrogen-deficient conditions. In order to minimize losses and maximize fruit yield, there is often an occurrence of excessive fertilization, soil structure degradation, and water pollution. Therefore, accurate and real-time monitoring of nitrogen content in fruit trees has become the fundamental prerequisite for precision management of orchards. Furthermore, precision orchard management is crucial for enhancing fruit quality by maintaining the optimal growth conditions necessary for trees. Moreover, it plays a vital role in safeguarding the ecological environment by mitigating the overuse of fertilizers and pesticides. With the continuous development and application of spectral remote sensing technology in agricultural monitoring and land management, this technology can provide an effective method for monitoring nitrogen content. Based on a review of relevant literature, this paper summarizes a research framework for monitoring and inversion of nitrogen content in fruit trees, which provides help for further research. Firstly, based on different remote sensing platforms, the application was discussed, on spectral remote sensing technology in the acquisition of nitrogen content in fruit trees. Secondly, the index parameters that can reflect the nitrogen content of fruit trees are summarized, which provides practical guidance for remote sensing monitoring. Additionally, the regression algorithms and application situations based on spectral data for nitrogen content were introduced. In conclusion, in response to the current issues and technological limitations, future research should focus on studying the nitrogen content characteristics of fruit trees during different phenological periods, integrating multi-type data information, and thereby improving the universality of the nitrogen content inversion model for fruit trees.

ABSTRACT: Chickens’ behaviors and activities are important information for managing animal health and welfare in commercial poultry houses. In this study, convolutional neural networks (CNN) models were developed to monitor the chicken activity index. A dataset consisting of 1,500 top-view images was utilized to construct tracking models, with 900 images allocated for training, 300 for validation, and 300 for testing. Six different CNN models were developed, based on YOLOv5, YOLOv8, ByteTrack, DeepSORT, and StrongSORT. The final results demonstrated that the combination of YOLOv8 and DeepSORT exhibited the highest performance, achieving a multiobject tracking accuracy (MOTA) of 94%. Further application of the optimal model could facilitate the detection of abnormal behaviors such as smothering and piling, and enabled the quantification of flock activity into 3 levels (low, medium, and high) to evaluate footpad health states in the flock. This research underscores the application of deep learning in monitoring poultry activity index for assessing animal health and welfare.

OBJECTIVE: To explore the application effect of pathway-based management of recombinant human erythropoietin ( rhEPO) injection during orthopedic perioperative period in orthopedics under the reform of China Healthcare Security Diagnosis Related Groups ( CHS-DRG) payment system. METHODS: Delphi method was employed, and two rounds of plan-do-check-act (PDCA) cycles were conducted to formulate the drug therapy pathway for rhEPO. Patients hospitalized in orthopedics of the hospital from Mar. to Jul. 2022 were extracted as the premanagement group, and patients from Mar. to Jul. 2023 were extracted as the post-management group. Eight DRG categories with the highest use rate of rhEPO were selected, and the improvement degree of benefit indicators (average hospitalization cost, average drug cost, average length of stay, rational use rate of rhEPO, blood transfusion rate) before and after management was compared. RESULTS: Compared with before management, the average hospitalization cost of orthopedic inpatients after management decreased from 58 829. 79 yuan to 48 259. 29 yuan (P<0. 05), the average drug cost decreased from 3 311. 23 yuan to 2 987. 52 yuan (P<0. 05), the average length of stay reduced from 10. 1 d to 8. 05 d (P<0. 01), with statistically significant differences. The rational use rate of rhEPO increased from 26. 27% (62 / 236) to 91. 54% (238 / 260), blood transfusion rate decreased from 34. 66% (165 / 476) to 25. 57% (169 / 661). CONCLUSIONS: By implementing pathway-based management and formulating standardized drug protocols, resource allocation has been optimized, medical quality has been improved, and the benefit indicators of DRG categories have been significantly enhanced. [ABSTRACT FROM AUTHOR]

Dairy industry is a symbolic industry of agricultural modernization and a strategic industry of coordinated development of primary, secondary and tertiary industries. Scaled-dairy farms' raw milk is the key of the core competence in the dairy industry. At present, China's scaled-dairy farms are struggling to milk prices fall, costs climb and net operating losses. This paper analyzes the strategies for cost saving, quality improving and effectiveness increasing in scaled dairy farms from precision feeding, precision nutrition, precision health and precision environmental in order to provide reference for the development of the national dairy industry. [ABSTRACT FROM AUTHOR]

In the pursuit of investigating the intricate interplay among enterprise digital transformation, supply chain performance, and precision management within the context of the era of big data, this research undertakes an empirical inquiry employing a structured questionnaire survey. This research commences by scrutinizing the impact of digital transformation on enterprise supply chain (ESC) performance, elucidating the strategic application of precision management in the digital transformation process, and subsequently offers comprehensive elucidations encompassing digital transformation, ESC performance, and precision management from diverse analytical perspectives. Subsequently, an array of research hypotheses is postulated and systematically substantiated through rigorous data analysis. Notably, the outcomes of the experimentation attest to the robustness of the questionnaire designed for this research, manifesting Cronbach's α coefficients that consistently surpass the threshold of 0.8 across all dimensions. Moreover, the Kaiser-Meyer-Olkin (KMO) values, surpassing 0.82, further endorse the instrument's reliability and validity, rendering it eminently suitable for experimental deployment. Subsequently, in the correlation analysis, a statistically significant relationship is discerned between the dual dimensions of digital transformation and the dual facets of ESC performance and precision management, ultimately affirming the validity of hypotheses H1a, H1b, H2a, H2b, H3a, H3b, H4a, and H4b. In the regression analysis, the coefficients of determination for the two regression models are 0.478 and 0.557, further confirming the hypotheses proposed in this research. The conclusion suggests that digital transformation is an evolving process, and enterprises should adopt a stance of continuous improvement and innovation. Regular assessments of the effectiveness of digital transformation should be conducted, with adjustments and improvements made based on feedback results. Therefore, this research provides valuable insights into the analysis of the relationship between digital transformation and ESC and precision management. [ABSTRACT FROM AUTHOR]

Precision Swine Farming has the potential to directly benefit swine health and industry profit by automatically monitoring the growth and health of pigs. We introduce the first system to use structural vibration to track animals and the first system for automated characterization of piglet group activities, including nursing, sleeping, and active times. PigSense uses physical knowledge of the structural vibration characteristics caused by pig-activity-induced load changes to recognize different behaviors of the sowand piglets. For our system to survive the harsh environment of the farrowing pen for three months, we designed simple, durable sensors for physical fault tolerance, then installed many of them, pooling their data to achieve algorithmic fault tolerance even when some do stop working. The key focus of this work was to create a robust system that can withstand challenging environments, has limited installation and maintenance requirements, and uses domain knowledge to precisely detect a variety of swine activities in noisy conditionswhile remaining flexible enough to adapt to future activities and applications. We provided an extensive analysis and evaluation of all-round swine activities and scenarios from our one-year field deployment across two pig farms in Thailand and the USA. To help assess the risk of crushing, farrowing sicknesses, and poormaternal behaviors, PigSense achieves an average of 97.8% and 94% for sow posture and motion monitoring, respectively, and an average of 96% and 71% for ingestion and excretion detection. To help farmers monitor piglet feeding, starvation, and illness, PigSense achieves an average of 87.7%, 89.4%, and 81.9% in predicting different levels of nursing, sleeping, and being active, respectively. In addition, we show that our monitoring of signal energy changes allows the prediction of farrowing in advance, as well as status tracking during the farrowing process and on the occasion of farrowing issues. Furthermore, PigSense also predicts the daily pattern and weight gain in the lactation cycle with 89% accuracy, a metric that can be used to monitor the piglets' growth progress over the lactation cycle. [ABSTRACT FROM AUTHOR]

The efficient management of irrigation water can affect crop profitability quite significantly. The application of precision irrigation based on soil monitoring can help manage water resources. In viticulture, the irrigation technique is thought to strongly influence grape ripening and the final grape composition. In this study, an irrigation decision support system was compared to a surface drip irrigation system in a commercial vineyard located in Andrea (Southern Italy) planted with Vitis vinifera cv. Montepulciano. We aimed to investigate the ability of the DSS to save water while maintaining an acceptable yield and quality of the grapes. To allow for the comparison, eco-physiological as well as yield parameters were measured during the irrigation periods in both irrigation systems over two years (2019 and 2020). The results indicate that the vines grown using the DSS treatment were less stressed compared to the plants grown using farm irrigation in both years. The yield attributes showed slight or no significant differences between the treatments. The quality results showed no significant differences between the treatments in both years. Our results indicate that with savings of 10% and 17% of the irrigation water in the first and second year, respectively, the DSS was able to maintain good yield and quality levels as compared to the farm irrigation system. These two-year results provide a promising implementation of its use in precision irrigation. [ABSTRACT FROM AUTHOR]

Accurate and prompt estimation of geospatial soybean yield (SY) is critical for the producers to determine key factors influencing crop growth for improved precision management decisions. This study aims to quantify the impacts of soybean cyst nematode (SCN) infestation on soybean production and the yield of susceptible and resistant seed varieties. Susceptible varieties showed lower yield and crop vigor recovery, and high SCN population (20 to 1080) compared to resistant varieties (SCN populations: 0 to 340). High-resolution (1.3 cm/pixel) aerial multispectral imagery showed the blue band reflectance (r = 0.58) and Green Normalized Difference Vegetation Index (GNDVI, r = −0.6) have the best correlation with the SCN populations. While GDNVI, Green Chlorophyll Index (GCI), and Normalized Difference Red Edge Index (NDRE) were the best differentiators of plant vigor and had the highest correlation with SY (r = 0.59–0.75). Reflectance (REF) and VIs were then used for SY estimation using two statistical and four machine learning (ML) models at 10 different train–test data split ratios (50:50–95:5). The ML models and train–test data split ratio had significant impacts on SY estimation accuracy. Random forest (RF) was the best and consistently performing model (r: 0.84–0.97, rRMSE: 8.72–20%), while a higher train–test split ratio lowered the performances of the ML models. The 95:5 train–test ratio showed the best performance across all the models, which may be a suitable ratio for modeling over smaller or medium-sized datasets. Such insights derived using high spatial resolution data can be utilized to implement precision crop protective operations for enhanced soybean yield and productivity.

Sheep management and production enhancement are difficult for farmers due to the lack of dynamic response and poor welfare of the sheep. Poor welfare needs to be mitigated, and each farm must receive an expert-level assessment of critical importance. To mitigate poor welfare, researchers have conducted machine learning-based studies to automate the sheep health behavior monitoring process instead of using manual assessment. However, failure to recognize some sheep health behaviors degrades the performance of the model. In addition, behavior challenges, parameters, and analysis must be considered when conducting a study based on machine learning. In this paper, we discuss the different challenges: what are the parameters of the sheep health behaviors, and how to analyze the sheep health behaviors for automated machine learning systems to be helpful in the long term? The hypothesis is based on a different review of the literature of precision-based animal welfare monitoring systems with the potential to improve management and production.

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Normalized canopy area is a measure of spatial canopy fill or canopy coverage in a tree. It is an important factor for growers to consider in making various farming decisions, such as timing and amount of fertilization, pesticide application, and irrigation, as it gives a sense of the overall canopy growth. This study focused on estimating the normalized canopy area of apple trees using a ground-based stereo-vision system in a commercial orchard under natural lighting conditions. A color, depth, and height threshold, followed by a point density-based outlier removal was applied to segment the target tree canopies. The segmented canopy area was compared with the area estimated with manual segmentation, which showed an F1 score of 0.78. The overall growth of the tree canopies was then represented using normalized canopy area, which was calculated as a fraction of pixels occupied by the tree foliage over the total possible area a tree could occupy for a given tree spacing. It was found that the normalized canopy area of individual trees was highly correlated with the surface volume of the canopy (r = 0.7), which is a traditionally used parameter to assess or represent canopy growth. The estimated normalized canopy area was then compared against experts' nitrogen recommendation for the sample trees, which was based on their visual assessment of the vigor/growth of the trees. Four individual experts (one apple farmer, one orchard manager, and two horticulturists with research and outreach experience) were asked to provide their independent nitrogen recommendation and an average recommendation level was calculated. The comparison showed an overall correlation coefficient of -0.5, indicating an opposite relationship (higher the canopy density, lower the need for nitrogen and vice versa) between the normalized canopy area and the average of the experts' nitrogen recommendation. When the relationship between the normalized area and nitrogen recommendation level was assessed for individual experts, the correlation coefficient was estimated to be -0.86, -0.84, -0.96, and -0.78, respectively, for Expert1, Expert2, Expert3, and Expert4 respectively. The results from this study identified a critical measure for assessing nitrogen needs at individual tree levels in apple orchards and provides an automated tool to estimate the measure. The outcomes of this study could be used as a part of an integrated decision support system for orchard operations (e.g., fertilization or irrigation) that may include additional canopy characteristics such as spectral signature, trunk diameter, and rate of change of leaf color in the fall.

Use of a grazing system that takes into account the ingestive behavior of animals and the physiological characteristics of plants maximizes land use and promotes positive effects on the environment. In this sense, the objective of this study was to evaluate the performance of Pantaneira breed cows, kept in rotated grazing in Mombasa grass (Megathyrsus maximum), with different grazing times. Fifty animals were divided into two treatments: Continuous T1: 24 h and T2—Inverted: 12 h. The experiment lasted 98 days, being determined the production and nutritional quality of the forage, digestibility, intake, and performance of the animals. The design was randomized blocks at 5% probability, with the means compared by the F test. The design used was completely randomized at 5% probability by the T test. There was no significant difference for biomass production (P > 0.05). However, after grazing the Inverted group, the forage showed a lower percentage of leaves and an increase in neutral detergent fiber and acid contents and total carbohydrates, and a reduction in crude protein and ether extract values, as well as a higher digestibility (P < 0.05). The animals of the Continuous group intaked more forage (P < 0.05). The Inverted group had a higher average daily weight gain, in addition to a lower feed conversion (P < 0.001). The animals in the Control group spent more time eating (P < 0.0001) and sleeping (P < 0.0001), while the animals of the Inverted group spent more time in idleness (P < 0.0001) and interacting (P < 0.05). There was no difference in rumination time between treatments (P > 0.05). It was concluded that Inverted grazing improved the quality of Mombasa grass and the performance of cows. [ABSTRACT FROM AUTHOR]

Tree of heaven (Ailanthus altissima) is a highly invasive tree species in the USA and the preferred host of an invasive insect, the spotted lanternfly (Lycorma delicatula). Currently, pest managers rely solely on ground surveys for detecting both A. altissima and spotted lanternflies. This study aimed to develop efficient tools for A. altissima detection using drones equipped with optical sensors. Aerial surveys were conducted to determine the optimal season, sensor type, and flight altitudes for A. altissima detection. The results revealed that A. altissima can be detected during different seasons and at specific flight heights. Male inflorescences were identifiable using an RGB sensor in the spring at A. altissima. This study suggests that employing drones with optical sensors can provide a near real-time and efficient method for A. altissima detection. Such a tool has the potential to aid in the development of effective strategies for monitoring spotted lanternflies and managing A. altissima.

Aflatoxins (AFs) are carcinogenic toxins produced by fungi in many staple grain crops. FDA legislative thresholds for the maximum allowable average AFs concentrations in grain intended for different uses determine if whole batches of grain are rejected or must be used for a less lucrative purpose. Few spatial studies of aflatoxin contamination in the field have been undertaken as AFs analysis is expensive, but such studies are key to improving food security. High risk areas need to be identified to allow management strategies to reduce the amount of wasted grain. AFs production tends to be greatest in high temperature and drought conditions. Previous research has suggested that local Moran's I (LMI) analysis of weather and remotely sensed imagery can identify locations at high risk of AFs contamination but this has not been validated with AFs data. This study uses a 1977-2004 AFs survey of counties in southern Georgia, USA with NDVI and thermal IR data from 1990-2004 imagery to validate this approach. Also investigated in this paper, as they are integral to the validation approach used, are issues relating to the identification of corn growing pixels in historical imagery and a modified kriging approach to interpolate the sparse AFs data for individual years. Comparison tests showed that significantly more high-risk points were identified by bivariate LMI analysis of thermal IR and NDVI in high-risk years than low and medium risk years and these pixels had significantly higher AFs concentrations than other pixels. AFs concentrations were not significantly higher for high-risk clusters in low and medium risk years. LMI analysis from all years suggested that imagery data was better at identifying drought risk than interpolated weather data alone as it also shows the effects of droughty soils. The approach developed here could be used with CropScape data, historic remotely sensed imagery and weather data to determine risk zones for other states in the south or other crops in Georgia. The approach developed for kriging sparse data could also be useful in a range of other environmental studies.

Papillary thyroid cancer is the most prevalent and indolent thyroid cancer, but still, around 20% of cases will develop regional recurrence or distant metastasis. Isolated or coexistent BRAF V600E and telomerase reverse transcriptase promotor (pTERT) mutations in thyroid cancer are associated with poor clinical outcomes. The prior knowledge of BRAF V600E and pTERT mutation may help to identify the cases that may recur or become refractory to standard radioactive iodine treatment. Such cases could be treated initially with complete disease eradication through extensive surgery followed by maximum permissible high-dose radioactive I-131 ablation and vigilant follow-up. Conventional risk assessment coupled with genotype-based risk assessment can help in the precise management of aggressive thyroid cancers. [ABSTRACT FROM AUTHOR]

Copyright of Agrochemistry & Soil Science / Agrokémia és Talajtan is the property of Akademiai Kiado and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Contamination of crops by aflatoxins (AFs) is a real risk in the South-Eastern USA. Contamination risk at the county level based on soil type and weather in different years has been investigated. However, defining AFs contamination risk zones within fields has not yet been attempted. Drought conditions, particularly within the month of June have been linked to high levels of AFs contamination at the county level. Soil characteristics and topography are the factors influencing drought status that vary most within fields. Here, soil, plant, topography and remotely sensed information are used to define AFs contamination risk zones within two fields using different approaches. Normalized difference vegetation index (NDVI) data were used to indicate potential droughty areas and thermal IR data from LandSat imagery were used to identify hot areas. Topographic variables were also computed. Comparison tests showed that a combination of regression analysis of soil, plant and imagery data and bi-variate local Moran's I analysis of NDVI and Thermal IR data from several years was the best way to define zones for mean and maximum AFs levels. An approach based on principal components analysis of soil, plant and imagery data from 2010, a high-risk year, was best for defining zones for minimum AFs levels. Analysis of imagery from several years suggested that the zones are likely to be relatively stable in time and could be defined using only freely available sensor, topographic and soil series data. Once defined, such zones can be managed to increase profitability and reduce waste. [ABSTRACT FROM AUTHOR]

Simple Summary: The early prediction of lambing dates for ewes could improve their management at critical time points during pregnancy and throughout lambing. A pilot study used proximity sensors to measure the interactions between ewes and rams during mating to predict conception and lambing dates successfully. This study is an extension of the pilot study using multiple rams under commercial conditions. Lambing dates were predicted based on peak ewe–ram interactions during mating and then compared to the actual lambing dates observed in the paddock. Overall, 91% of lambing dates were predicted within ±6 days and 84% within ±3 days. The use of proximity sensors during mating was an effective means of predicting lambing dates. This could help improve ewe management during pregnancy and lambing and the subsequent survival and performance of their lambs. Ewes often lamb over extended periods so the level of nutrition during pregnancy and lambing may be suboptimal for ewes that conceived later during mating. Predicting lambing dates would allow cohorts of ewes with similar gestational ages to be managed more precisely to achieve targets for ewe nutrition, feed on offer, mob sizes and access to shelter to improve lamb survival. The interactions between ewes and rams during mating have been used to predict the time of oestrus and lambing dates successfully, but this has not been tested at a commercial scale. In this study, proximity sensors were used to measure interactions between inexperienced Merino ewes (n = 317) and experienced rams (n = 9) during a 27-day mating period under commercial production conditions. When the gestation length was assumed to be 150 days, 91% of lambing dates were predicted within ±6 days of the actual birth date of lambs and 84% of lambing dates were predicted within ±3 days. The use of proximity sensors during mating was an effective means of predicting lambing dates, and there was no significant difference in accuracy for single bearing verses multiple bearing ewes. However, DNA parentage data showed the ram corresponding with the maximum daily interactions ratio was the sire for only 16% of all progeny, suggesting they could not be used to indicate the sire of the progeny. [ABSTRACT FROM AUTHOR]

Reflectance-based vegetation indices can be valuable for assessing crop nitrogen (N) status and predicting grain yield. While proximal sensors have been widely studied in agriculture, there is increasing interest in utilizing aerial sensors. Given that few studies have compared aerial and proximal sensors, the objective of this study was to quantitatively compare the sensitivity of aerially sensed Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red-Edge Index (NDRE) and proximally sensed NDVI for assessing total N uptake at panicle initiation (PI-NUP) and predicting grain yield in rice. Nitrogen response trials were established over a 3-year period (10 site-years) at various locations throughout the Sacramento Valley rice growing region of California. At PI, a multispectral unmanned aircraft system (UAS) was used to measure NDVIUAS and NDREUAS (average ground sampling distance: 3.7 cm pixel−1), and a proximal GreenSeeker (GS) sensor was used to record NDVIGS. To enable direct comparisons across the different indices on an equivalent numeric scale, each index was normalized by calculating the Sufficiency-Index (SI) relative to a non-N-limiting plot. Kernel density distributions indicated that NDVIUAS had a narrower range of values that were poorly differentiated compared to NDVIGS and NDREUAS. The critical PI-NUP where yields did not increase with higher PI-NUP averaged 109 kg N ha−1 (±4 kg N ha−1). The relationship between SI and PI-NUP for the NDVIUAS saturated lower than this critical PI-NUP (96 kg N ha−1), whereas NDVIGS and NDREUAS saturated at 111 and 130 kg N ha−1, respectively. This indicates that NDVIUAS was less suitable for making N management decisions at this crop stage than NDVIGS and NDREUAS. Linear mixed effects models were developed to evaluate how well each SI measured at PI was able to predict grain yield. The NDVIUAS was least sensitive to variation in yields as reflected by having the highest slope (2.4 Mg ha−1 per 0.1 SI). In contrast, the slopes for NDVIGS and NDREUAS were 0.9 and 1.1 Mg ha−1 per 0.1 SI, respectively, indicating greater sensitivity to yields. Altogether, these results indicate that the ability of vegetation indices to inform crop management decisions depends on the index and the measurement platform used. Both NDVIGS and NDREUAS produced measurements sensitive enough to inform N fertilizer management in this system, whereas NDVIUAS was more limited. [ABSTRACT FROM AUTHOR]

A 4 years field study was conducted to improve nitrogen (N) and phosphorus (P) use efficiencies in wheat (Triticum aestivum L.) using arbuscular mycorrhizal fungi (AMF) and precision N management. Split plot design was employed to rationalize N and P fertilizer use in two different experiments with AMF in the main plot and fertilizer application practice in the subplots. The AMF seed coating improved mycorrhization, root dry weight and P uptake. AMF coating of wheat seeds improved mean grain yield and agronomic efficiency of applied P fertilizer (AEP) by 11% and 55.4%, respectively. However, AMF seed coating did not improve chlorophyll content, normalized difference vegetative index, grain yield and agronomic efficiency of applied N fertilizer (AEN). Precision N management using leaf color chart (LCC), chlorophyll meter (SPAD), and GreenSeeker optical sensor (GS) sustained wheat grain yield equivalent to the soil-test based N fertilizer recommendation with 20–31 kg ha−1 less fertilizer N. Proximal sensing guided N use improved AEN by 21.9%–39.6% while mitigating global warming potential by 16%–24%. Integrated use of AMF seed coating and precision N management provides a potential solution to improve N and P nutrition and reduce environmental footprints of fertilizer N use in wheat. [ABSTRACT FROM AUTHOR]

The rice–wheat cropping system (RWCS) is the backbone of Indian farming, especially in the north-western region. But continuous adoption of the RWCS in northwest India has resulted in major challenges and stagnation in the productivity of this system. Additionally, the Indo-Gangetic Plains of Pakistan, Nepal, and Bangladesh are also facing similar challenges for sustainable production of the RWCS. Several emerging problems, such as the exhausting nutrient pool in soil, deteriorating soil health, groundwater depletion, escalating production cost, labor scarcity, environmental pollution due to crop residue burning and enhanced greenhouse gas emissions, climatic vulnerabilities, and herbicide resistance in weed species, are a few major threats to its sustainability. To address these challenges, a wide range of sustainable intensification technologies have been developed to reduce the irrigation and labor requirements, tillage intensity, and straw burning. Awareness and capacity building of the stakeholders and policy matching/advocacy need to be prioritized to adopt time- and need-based strategies at the ground level to combat these challenges. This review summarizes the current status and challenges of the RWCS in the northwest region of the country and also focuses on the precision management options for achieving high productivity, profitability, and sustainability. [ABSTRACT FROM AUTHOR]

Water management is one of the most important operations in greenhouse baby leaf production. However, growers mainly irrigate the plants based on experience, which generally leads to yield loss, uneven quality, and low water-use efficiency. This study evaluated four evapotranspiration (ET) models, such as Radsum, Penman methods, FAO Penman-Monteith, and Priestley-Taylor, for irrigation strategy by predicting the ET level of greenhouse baby pakchoi [Brassica rapa L. ssp. chinensis (L.) Hanelt] under different plant densities (72-, 128-, 200-, and 288-plug tray). Among environmental factors, net radiation and photosynthetically active radiation (PAR) had the highest correlation with ET, with R2 of 0.93 and 0.94, respectively. Plant growth period was divided into different stages according to canopy development and substrate surface coverage. The corresponding crop coefficient (Kc) was introduced into ET prediction models. The result shows overestimation of ETc (crop evapotranspiration) by the Radsum and Penman methods. FAO Penman-Monteith and Priestley-Taylor methods performed the best with R2 ≈0.7 for all planting densities. These two methods are recommended for greenhouse irrigation scheduling in baby pakchoi production.

The rice–wheat cropping system (RWCS) is the backbone of Indian farming, especially in the north-western region. But continuous adoption of the RWCS in northwest India has resulted in major challenges and stagnation in the productivity of this system. Additionally, the Indo-Gangetic Plains of Pakistan, Nepal, and Bangladesh are also facing similar challenges for sustainable production of the RWCS. Several emerging problems, such as the exhausting nutrient pool in soil, deteriorating soil health, groundwater depletion, escalating production cost, labor scarcity, environmental pollution due to crop residue burning and enhanced greenhouse gas emissions, climatic vulnerabilities, and herbicide resistance in weed species, are a few major threats to its sustainability. To address these challenges, a wide range of sustainable intensification technologies have been developed to reduce the irrigation and labor requirements, tillage intensity, and straw burning. Awareness and capacity building of the stakeholders and policy matching/advocacy need to be prioritized to adopt time- and need-based strategies at the ground level to combat these challenges. This review summarizes the current status and challenges of the RWCS in the northwest region of the country and also focuses on the precision management options for achieving high productivity, profitability, and sustainability.

An abbreviated history of genetic evaluation in Merinos demonstrates the remarkable changes that have occurred over the past 50 years. The amount of information available from an increasing number of studs adopting detailed measurement and recording is enormous and much effort has been directed at encouraging selection practices in studs on the basis of this information. In the past we have relied on genetic gains made in the ram breeding sector to be transferred on average to ram buyers, after a lag, for the benefit of the industry. But ram buyers can markedly influence their productivity by being active participants in genetic evaluation rather than relying on gains trickling down on average from ram breeders. Strategies for commercial producers involving choice of ram breeder(s), effective use of breeding values for ram purchases, and on-farm selection and management in the commercial flock are canvassed. [ABSTRACT FROM AUTHOR]

Copyright of Food & Machinery is the property of Food & Machinery Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Agrochemistry & Soil Science / Agrokémia és Talajtan is the property of Akademiai Kiado and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Hanwoo cattle are an important food source in Korea and their supply can have a major impact on meat availability for Korean consumers. The Hanwoo population was 1.8 million head in 2005 and gradually increased to 2.6 million in 2015. Per capita beef consumption has also increased, to 11.6 kg per year in 2015, and is expected to continue to increase. Because intramuscular fat percentage is a critical contributor to meat quality, Hanwoo cattle are fed a high-energy corn-based diet for long fattening periods. Long fed diet causes significant alterations in fat percentage in the loin muscle and other areas of the carcass. However, these long feeding periods increase feeding costs and beef prices. Recently, there has been increased Korean consumer demand for lean beef which has less fat, but is tender and priced more reasonably. These consumer demands on the Korean beef industry are driving differing beef production systems and also changes to the beef grading methodology. Korean government has made a significant investment to select bulls with favorable production traits using progeny testing. Progeny tested bull semen has been disseminated to all Hanwoo farmers. A beef traceability system has been employed for all cattle breeds in Korea since 2009. Hanwoo cattle are ear-marked with a 12-digit identification number from birth to slaughter. This number allows traceability of the management history of individual cattle, and also provides information to consumers. Traceability including management information such as herd, farm, year of birth, and carcass data can determine estimated breeding values of Hanwoo. For a sustainable Hanwoo industry, research scientists in Korea have attempted to develop feeds for efficient fattening periods and precision feeding systems based on genetic information for Hanwoo cattle. These initiatives aim to Korean consumer demands for beef and provide more precision management in beef production in Korea.

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Highlights • LSTM-RNN model was used to monitor and classify cattle behavior patterns using inertial measurement unit. • Eight behavior patterns including social behavior were classified. • Classify accuracy of periodic and regular behaviors were higher than irregular and fluctuant behaviors. • The LSTM-RNN models achieved their best performance when using window-sizes 64. Abstract In this study, we aimed to develop a recurrent neural network (RNN) with a long short-time memory (LSTM) model to monitor and classify cattle behavior patterns using inertial measurement units (IMU). This model was trained using motion data obtained from 6 Japanese steers. Each steer was fitted with an IMU sensor inside a waterproof box attached to a collar on the neck. Classified behavior classes included feeding, lying, ruminating (while lying), ruminating (while standing), licking salt, moving, social licking and head butt. LSTM-RNN model was trained to classify and measure cattle's behavior across three window-sizes including window-size 64, 128 and 256 (3.2 s, 6.4 s and 12.8 s). A convolution neural network (CNN) model was used for comparison. The results reveal the LSTM-RNN model classification performance was superior to the CNN model. The LSTM-RNN model was found to achieve the best performance when using a window-size of 64 (accuracy, precision, recall, f1-score all were 88.7%). With a window-size 64, classification accuracy of specific behaviors was 97.8% (feeding), 88.7% (lying), 88.4% (ruminating-lying), 92.9% (ruminating-standing), 94.4% (licking salt), 84.8% (moving), 80.3% (social licking), and 81.9% (head butt). A few physically similar behaviors were easily misclassified. In conclusion, the LSTM-RNN demonstrated reasonable classification of cattle behavior. In future, additional sensors, such as a microphone, could be added to the cattle behavior monitoring system and behavior classification extended to cattle welfare and growth behaviors, such as feeding, reproduction and disease prediction. [ABSTRACT FROM AUTHOR]

Copyright of Nursing of Integrated Traditional Chinese & Western Medicine is the property of Journal of Clinical Nursing in Practice (Editorial Board, Shanghai Jiao Tong University Press) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Estimating the spatial distribution of weeds for site-specific control is essential. Therefore, this research was conducted to predict and interpolate the spatial distribution of Amaranthus retroflexus L. populations using a Radial Basis Function Neural Network (RBF-NN) in two potato fields. Weed population data were collected from sampling 200 and 36 points, respectively, in two commercial potato fields in Jolge Rokh, of Torbat Heidarieh in Khorasan Razavi and Mojen of Shahroud in Semnan Provinces, Iran, in 2012. Some statistical tests, such as comparisons of the means, variance and statistical distribution, as well as linear regression, were used for the observed point sample data and the estimated weed seedling density surfaces to evaluate the neural network capability for predicting the spatial distribution of the weed. The results showed that the trained RBF-NN had high capability in the spatial prediction in points that were not sampled with 100% output, 0.999 coefficients, and an average error of less than 0.04 and 0.07 in the Mojen and Jolge Rokh Regions, respectively. Test results also showed that there was no significant difference between the statistical characteristics of actual data and the values predicted by the RBF-NN. According to the experimental results, the RBFNN can be used as an alternative method to estimate the spatial changes function of annual weeds with random dispersion, such as Redroot Pigweed. [ABSTRACT FROM AUTHOR]

In order to effectively solve series of problems which affect grain production in implementation of the “Bohai granary” project, such as the wide varieties of factors (including soil fertility, soil salinity, pH, underground water level, water salinity, crop varieties and management etc.), the huge spatial and temporal variability, the difficulty of real-time information collection, the complexity of analysis and processing etc., a “Bohai granary” big data platform is developed. With the invention of “Shennong IOAT” series of sensing equipment, the acquisition of multi-site real-time synchronization field information is achieved. And by combining a variety of data collection methods, the collection of factors comprehensively affecting the grain production has been realized. Based on the Hadoop big data platform, it becomes a reality to store, analyze and apply the collected data efficiently. Through the big data platform, the precise management of grain production, the automatic warning of soil moisture content, the disaster and typical insect damage in crop growth, and the automatic control of typical link production are all achieved. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Animal Science is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: Catheter-associated urinary tract infection (CAUTI) is an important cause of prolonged hospital stay, which increases economic and medical burden for patients and hospitals, and it is a key focus of hospital infection prevention and control. However, there are currently few studies that convert evidence-based scientific evidence on CAUTI prevention and control into clinical applications and evaluation on its practical effects in combination with standardized infection ratio (SIR), the critical indicator of infection prevention and control. This study aims to establish a precision management plan for reducing the incidence of CAUTI, driven by the findings of a comprehensive evidence summary, to apply this plan across all the nursing units within the entire hospital, followed by a comparative analysis of CAUTI incidence, SIR, the average duration of indwelling urinary catheter for each patient, and the compliance rate on hand hygiene protocols for medical staff before and after the implementation of the precision management plan., Methods: Based on a comprehensive review of the best evidence for preventing CAUTI, a precision management plan was meticulously developed through panel discussions and 2 rounds of expert consultations using Delphi technique. Subsequently, a historical control study was conducted to evaluate the plan's effectiveness. A total of 17 658 patients with indwelling urinary catheter in inpatient departments from January to December 2021 comprised the control group. These patients received standard nursing measures for CAUTI. Another 18 753 patients with indwelling urinary catheters in the inpatient departments from January to December 2022 comprised the intervention group, underwent the precision management scheme based on the best available evidence, to enhance CAUTI prevention. The incidence and SIR of CAUTI, the average duration of indwelling urinary catheter for each patient, and the compliance rate on hand hygiene protocols for medical staff were compared between the 2 groups., Results: Compared with the control group, the incidence of CAUTI in the intervention group was significantly decreased (0.48‰ vs 1.12‰, χ 2 =20.814, P <0.001), SIR was decreased in the intervention group (0.55 vs 1.37); the average duration of indwelling urinary catheter for each patient was significantly decreased [(4.33±1.55) d vs (4.43±1.79) d, t =11.941, P <0.001]. The ratio of compliance rate of medical staff with strict hand hygiene protocols higher than 95% in the intervention group was significantly higher than that in the control group (93.3% vs 83.3%, χ 2 =5.822, P =0.016)., Conclusions: The implementation of the precision management plan for reducing CAUTI based on a summary of the best available evidence on CAUTI prevention and control in patients with indwelling urinary catheters has found to be effective. This approach significantly reduces the incidence of CAUTI, reduces the average duration of indwelling urinary catheter, and enhances hand hygiene compliance among medical staff. It provides a scientific and efficient strategy for preventing and controlling CAUTI in the hospital, ultimately saving patients from unnecessary medical expense.

Precision management systems for livestock offer the potential to monitor and manage animals on an individual basis. A key component of these sensor based systems are the analytical models that automatically translate sensor data into different behavioral categories. A new methodology was proposed for multi-class behavior modeling based upon the “one-vs-all” framework. This methodology differs from the standard approach to behavior classification where a single classifier is trained to discriminate between multiple behaviors. Instead a set of binary classifiers are trained to each discriminate one of the behavior classes against a combined class of all the remaining behaviors. The confidence scores from the set of binary classifiers are then combined to generate a behavioral estimate. The performance of this new modeling approach is validated across a study involving 24 Holstein-Friesian dairy cows that were each fitted with an Inertial Measurement Unit (IMU) on a collar upon their neck. Five general classes of cow behavior grazing, walking, ruminating, resting and “Other” were classified. Binary time series classifiers were tailored to each of the five behaviors through training and validation of each model across a range of configurations including nine window sizes, five classifiers and a feature selection process of 84 candidate input features. Results revealed that the proposed model classified grazing behavior with an extremely high classification accuracy ( F -score of 0.98), whilst ruminating and resting behaviors were also classified with a high accuracy ( F -scores of 0.87 and 0.85, respectively), and walking was classified with a lower accuracy ( F -score of 0.73). The proposed model offered a 5% performance improvement over standard multi-class time series classifiers that was attributed to the “one-vs-all” framework training a classifier for each behavior independently; this created diversity in the behavior model. The feature selection process used in developing each of the binary classifiers found that features representing the motion intensity and pitch of the cow’s head were most important to each of behavior’s classification. Whilst a minor performance improvement was obtained using the proposed methodology, it is suggested that further performance improvements could be obtained by increasing the diversity of the classifier’s inputs. Diversity could be created by fusing the data of other sensors that can be fitted to the cows i.e. GPS tracking unit, pressure sensor and microphone. [ABSTRACT FROM AUTHOR]