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Nitrile Butadiene Rubber (NBR) is commonly used in ships' water-lubricated tail bearings. However, sediment in the water significantly affects these bearings' friction and wear performance. This study investigates NBR test blocks' friction and wear behavior in conjunction with ZCuSn10Zn2 copper ring friction pairs within a sediment-laden water lubrication environment. Two primary factors were considered: sediment particle concentration and sediment particle size. Friction and wear tests were conducted under pure water and sediment-laden conditions using the ZY-1 ring block friction and wear tester. The friction coefficients, wear quantities, and variations in mass concentrations and sediment particle sizes were measured and compared. The surface morphology of the test blocks was analyzed using a laser confocal microscope. The findings indicate that as sediment concentration increases, the particle size's impact on NBR's abrasive wear diminishes. The variation in particle size directly influences the number of particles that penetrate the interface between the friction partners and the nature of three-body wear. Conversely, changes in particle concentration primarily affect the extent of wear; specifically, both the wear volume and the average coefficient of friction of the NBR specimens increase with rising sediment concentration. The wear mechanisms observed on the surface of the NBR test blocks are predominantly characterized by micro-cutting, rolling wear, and the coexistence of both wear modes. This study offers valuable insights for the design and optimization of water-lubricated bearings. [ABSTRACT FROM AUTHOR]

The phenotypic analysis of wheat spikes plays an important role in wheat growth management, plant breeding, and yield estimation. However, the dense and tight arrangement of spikelets and grains on the spikes makes the phenotyping more challenging. This study proposed a rapid and accurate image-based method for in-field wheat spike phenotyping consisting of three steps: wheat spikelet segmentation, grain number classification, and total grain number counting. Wheat samples ranging from the early filling period to the mature period were involved in the study, including three varieties: Zhengmai 618, Yannong 19, and Sumai 8. In the first step, the in-field collected images of wheat spikes were optimized by perspective transformation, augmentation, and size reduction. The YOLOv8-seg instance segmentation model was used to segment spikelets from wheat spike images. In the second step, the number of grains in each spikelet was classified by a machine learning model like the Support Vector Machine (SVM) model, utilizing 52 image features extracted for each spikelet, involving shape, color, and texture features as the input. Finally, the total number of grains on each wheat spike was counted by adding the number of grains in the corresponding spikelets. The results showed that the YOLOv8-seg model achieved excellent segmentation performance, with an average precision (AP) @[0.50:0.95] and accuracy (A) of 0.858 and 100%. Meanwhile, the SVM model had good classification performance for the number of grains in spikelets, and the accuracy, precision, recall, and F1 score reached 0.855, 0.860, 0.865, and 0.863, respectively. Mean absolute error (MAE) and mean absolute percentage error (MAPE) were as low as 1.04 and 5% when counting the total number of grains in the frontal view wheat spike images. The proposed method meets the practical application requirements of obtaining trait parameters of wheat spikes and contributes to intelligent and non-destructive spike phenotyping. [ABSTRACT FROM AUTHOR]

The rapid and accurate acquisition of nitrogen, phosphorus and potassium nutrient contents in grape leaves is critical for improving grape yields and quality and for industrial development. In this study, crop growth was non-destructively monitored based on unmanned aerial vehicle (UAV) remote sensing technology. Three irrigation levels (W1, W2 and W3) and four fertilization levels (F3, F2, F1 and F0) were set in this study, and drip irrigation fertilization treatments adopted a complete block design. A correlation analysis was conducted using UAV multispectral image data obtained from 2019 to 2021 and the field-measured leaf nitrogen content (LNC), leaf potassium content (LKC) and leaf phosphorus content (LPC) values; from the results, the vegetation indices (VIs) that were sensitive to LNC, LKC and LPC were determined. By combining spectral indices with partial least squares (PLS), random forest (RF), support vector machine (SVM) and extreme learning machine (ELM) machine-learning algorithms, prediction models were established. Finally, the optimal combinations of spectral variables and machine learning models for predicting LNC, LPC and LKC in each grape growth period were determined. The results showed that: (1) there were high demands for nitrogen during the new shoot growth and flowering periods, potassium was the main nutrient absorbed in the fruit expansion period, and phosphorus was the main nutrient absorbed in the veraison and maturity periods; (2) combining multiple spectral variables with the RF, SVM and ELM models could result in improved LNC, LPC and LKC predictions. The optimal prediction model determination coefficient (R2) derived during the new shoot growth period was above 0.65, and that obtained during the other growth periods was above 0.75. The relative root mean square error (RRMSE) of the above models was below 0.20, and the Willmott consistency index (WIA) was above 0.88. In conclusion, UAV multispectral images have good application effects when predicting nutrient contents in grape leaves. This study can provide technical support for accurate vineyard nutrient management using UAV platforms. [ABSTRACT FROM AUTHOR]

This study aims to provide an effective image analysis method for clover detection and botanical composition (BC) estimation in clover–grass mixture fields. Three transfer learning methods, namely, fine-tuned DeepLab V3+, SegNet, and fully convolutional network-8s (FCN-8s), were utilized to detect clover fractions (on an area basis). The detected clover fraction (CF detected ), together with auxiliary variables, viz., measured clover height (H clover ) and grass height (H grass ), were used to build multiple linear regression (MLR) and back propagation neural network (BPNN) models for BC estimation. A total of 347 clover–grass images were used to build the estimation model on clover fraction and BC. Of the 347 samples, 226 images were augmented to 904 images for training, 25 were selected for validation, and the remaining 96 samples were used as an independent dataset for testing. Testing results showed that the intersection-over-union (IoU) values based on the DeepLab V3+, SegNet, and FCN-8s were 0.73, 0.57, and 0.60, respectively. The root mean square error (RMSE) values for the three transfer learning methods were 8.5, 10.6, and 10.0%. Subsequently, models based on BPNN and MLR were built to estimate BC, by using either CF detected only or CF detected , grass height, and clover height all together. Results showed that BPNN was generally superior to MLR in terms of estimating BC. The BPNN model only using CF detected had a RMSE of 8.7%. In contrast, the BPNN model using all three variables (CF detected , H clover , and H grass ) as inputs had an RMSE of 6.6%, implying that DeepLab V3+ together with BPNN can provide good estimation of BC and can offer a promising method for improving forage management. [ABSTRACT FROM AUTHOR]

Most film thickness measurement methods damage the working surface of a bearing and cannot measure the minimum film thickness, making it difficult to reveal the lubrication state and warn of wear. Two non-intrusive ultrasonic methods were proposed for measuring the film thickness distribution of the bearing, i.e., the full circumferential measurement and the prediction based on limited measuring points. The ultrasonic recognition model of film thickness was built. A film thickness measuring device and its calibration device were constructed. A calibration experiment in the range of 1–150 µm and a measurement experiment of the bearing's film thickness distribution were carried out. The results showed that in the calibration range, the relative error of most recognition values was less than ±5%, and some are less than 3%. The identification accuracy of the spring model has a zoned phenomenon. The relative difference between the experimental and the simulated values of the film thickness was less than 8% under most working conditions. The predicted values of eccentricity, attitude angle, and minimum film thickness have a small difference from the simulated values, indicating that the accuracy of the measurement method is high. [ABSTRACT FROM AUTHOR]

In the 1950s, a long-term experiment was initiated at three locations in Northern Sweden. The treatments included four cropping systems, which differed in the number of years with leys or annual crops in the crop rotation. To create awareness of the experiment as a research resource for further scientific studies, we summarise the history (experimental design, materials and methods, main measurements) and scientific findings of the experiment, as well as reflect on its usefulness and opportunities for further studies. So far, scientific focus has been on the effects of cropping systems on soil characteristics. The main findings indicate that soil porosity and hydraulic conductivity were greater in cropping systems with a large proportion of ley, soil bulk density showed the opposite trend. In terms of chemical properties, cropping systems incorporating more ley also have greater soil organic carbon content than annual crops systems, and both soil carbon and N storage decrease over time in cropping systems with only annual crops. The effects of cropping system on crop yields and quality are areas for further investigation. [ABSTRACT FROM AUTHOR]

The aim of this research was to compare two irrigation and N application systems, gun irrigation (GI) and drip fertigation (DF), in terms of soil water dynamics, N uptake, N use efficiency, and yield of table potatoes. Two treatments were set up in a three-year field experiment. Treatments differed by irrigation and N application methods, and scheduling. Water and N management in GI was managed according to typical best-practice while irrigation and N for DF was applied through drip fertigation according to a crop simulation model. Results showed that DF provided high soil water content at the center of the ridge. Average soil water content across the ridge soil profile was higher for DF than GI. Compared to DF, GI led to relatively high canopy growth early in the season through applying all N at planting, while the opposite trend was detected later in the growing season. Tuber yields in GI were 46, 43 and 44 t ha −1 in 2013, 2014 and 2015, respectively. Tuber yields in DF were 48, 43 and 40 t ha −1 in 2013, 2014 and 2015, respectively. There was no significant difference in yield between systems for any of the three seasons of the experiment. However, DF led to significantly higher tuber N recovery and agronomic N use efficiency in one of the years (2013). In addition, GI caused greater nitrate leaching during the growing season in both 2013 and 2014, as measured by suction cups and simulated by the Daisy simulation model. [ABSTRACT FROM AUTHOR]

Potato production ranks fourth in the world after rice, wheat, and maize and it is highly sensitive to water stress. It is thus very important to implement irrigation management strategies to minimize the effects of water stress under different climate conditions. The use of modelling tools to calculate the soil water balance on a daily basis has become widespread in the last decades. Therefore, this study was performed to simulate potato yield, dry matter and soil water content under different water stress condition using the AquaCrop model. Three levels of irrigation comprising full irrigated (I f ), deficit irrigated (I d ) and not irrigated (I 0 ) were investigated in three-years potato field experiment (2013–15) with four replicates in randomized complete block design. Tuber and total dry matter yield, canopy cover, dry matter production during the crop growth season, and soil water content was simulated using the AquaCrop model. Data from full irrigated treatment of 2014 was used for model calibration and data from 2013 (I f , I d , and I 0 treatments), 2014 (I d , and I 0 treatments) and 2015 (I f , I d , and I 0 treatments) were used for model validation. The sensitivity analysis of different parameters showed that K cTr , HI 0 , CCX, calendar day from sowing to start of senescence and WP * had the most pronounced influence on tuber yield. The result showed that the AquaCrop model simulated soil water content, canopy cover and above-ground dry matter during the crop growth seasons with acceptable accuracy, except for I 0 treatment. The means of the normalized root mean square error calculated for soil water content, dry matter during crop growth season and tuber yield for all irrigation treatments in validation years were 0.164, 0.157 and 0.046, respectively, indicating the ability of AquaCrop in simulation of soil water content and crop production. In conclusion, the AquaCrop model could be calibrated to simulate growth and yield of potato under temperate condition, reasonably well, and become a very useful tool to support decision on when and how much irrigate. [ABSTRACT FROM AUTHOR]

Understanding variations in sap flow rates and the environmental factors that influence sap flow is important for exploring grape water consumption patterns and developing reasonable greenhouse irrigation schedules. Three irrigation levels were established in this study: adequate irrigation (W1), moderate deficit irrigation (W2) and deficit irrigation (W3). Grape sap flow estimation models were constructed using partial least squares (PLS) and random forest (RF) algorithms, and the simulation accuracy and stability of these models were evaluated. The results showed that the daily mean sap flow rates in the W2 and W3 treatments were 14.65 and 46.94% lower, respectively, than those in the W1 treatment, indicating that the average daily sap flow rate increased gradually with an increase in the irrigation amount within a certain range. Based on model error and uncertainty analyses, the RF model had better simulation results in the different grape growth stages than the PLS model did. The coefficient of determination and Willmott's index of agreement for RF model exceeded 0.78 and 0.90, respectively, and this model had smaller root mean square error and d-factor (evaluation index of model uncertainty) values than the PLS model did, indicating that the RF model had higher prediction accuracy and was more stable. The relative importance of the model predictors was determined. Moreover, the RF model more comprehensively reflected the influence of meteorological factors and the moisture content in different soil layers on the sap flow rate than the PLS model did. In summary, the RF model accurately simulated sap flow rates, which is important for greenhouse grape irrigation. [ABSTRACT FROM AUTHOR]

The aim of this study was to address the problems of water scarcity and low water and fertilizer use efficiency in vineyards in the Guanzhong Plain of Shaanxi, China. In the experiment, drip fertigation technology was used, including three irrigation levels (W3 (100% M, and M as the irrigation quota), W2 (75% M) and W1 (50% M)) and four fertilization levels (F3 (648 kg/hm2), F2 (486 kg/hm2), F1 (324 kg/hm2) and F0 (0 kg/hm2)). Traditional fertilization (CG) and a rainfed (CK) treatments were used as control treatments. Field experiments were conducted in Yangling, Shaanxi, China from 2019 to 2021 (wet years in 2019 and 2020, and dry years in 2021) to determine irrigation and fertilization intervals corresponding to higher crop yields and water and fertilizer utilization efficiency in different precipitation patterns. The results showed that, compared with the CK and CG treatments, the drip irrigation and fertilization treatment increased grape yield and significantly improved water productivity and fertilizer agronomic use efficiency. The highest grape yields were obtained in the F2 treatment under drip fertigation, and the highest grape yields were 16044 kg/hm2 and 12728 kg/hm2 in wet years and dry years obtained at W1F2 and W2F2, respectively. With the same fertilization level (F2), the grape yields of the W1, W2 and W3 treatments in wet years increased by 37.95%, 23.73% and 23.50% compared with those in dry years, respectively. The optimal irrigation and fertilization intervals determined by the response surface method were: the irrigation amount ranged from 8 to 11 mm and 32–38 mm, nitrogen application ranged from 182 to 190 kg/hm2 and 162–164 kg/hm2, phosphorus application ranged from 116 to 131 kg/hm2 and 98–100 kg/hm2, and potassium application ranged from 216 to 225 kg/hm2 and 194–201 kg/hm2 in wet years and dry years, respectively. This provides a guideline for the precision management of irrigation and fertilization of grape under different precipitation year types in Guanzhong Plain of Shaanxi Province. ● Drip fertigation increased grape yield, WUE and FAUE. ● The optimal drip fertigation strategy for highest yield, WUE and FAUE was derived in wet and dry years. ● Response surface methodology was used to determine optimal irrigation and fertilization in wet and dry years. [ABSTRACT FROM AUTHOR]

Highlights • New non-linear dynamic WP* models were developed. • New insight of crop water production functions to develop new HI models. • Newly developed WP* and HI models improved yield simulation accuracy. Abstract Simulating yield response to different irrigation scenarios is important for agricultural production, especially in the arid region where agriculture depends heavily on irrigation. To better predict yield under different irrigation scenarios, the variation of normalized water productivity (WP*) over the whole growing period of maize for seed production and the effect of different irrigation treatments on harvest index (HI) were investigated using field experiments from 2012 to 2015 in an arid region of northwest China. Two new non-linear dynamic WP* (WP* KR-L and WP* KR-S) models derived from the Logistic and Sigmoid equations, and four new HI (HI KR-J , HI KR-M , HI KR-B and HI KR-S) models developed on the basis of water deficit multiplicative or additive models at different growth stages were compared with the measurements and the WP* (WP* AC) and HI sub-model (HI AC) in the original AquaCrop model (Version 4.0). In addition, the WP* AC and HI AC models in the original AquaCrop model were replaced by the optimal WP* and HI models to build the AquaCrop-KR model. Then the yield simulated by the AquaCrop-KR model was compared with the measured yield and the yield simulated by the original AquaCrop model. The results show that both WP* KR-L and WP* KR-S models improved the simulation of final biomass, especially for the WP* KR-L model. The tested HI sub-models, namely HI KR-J , HI KR-M , HI KR-B and HI KR-S models had good performance to simulate HI under different irrigation scenarios, and the HI KR-M model was the best among all tested sub-models. When both WP* KR-L and HI KR-M sub-models were embedded into Aquacrop, the performance of the AquaCrop model was improved significantly to simulate yield, especially under severe water stress condition, with R2 increased from 0.496 to 0.653, NRMSE decreased from 26.2% to 16.1% and EF increased from 0.055 to 0.642. [ABSTRACT FROM AUTHOR]

Growing potatoes in temperate regions often leads to nitrogen (N) leaching, however this could be significantly reduced with drip irrigation and nitrogen fertigation if the timing and amounts of the applications matched the crop demand. Determination of when and how much to apply is not straightforward, particularly because the N mineralisation during the potato-growing season is unknown. The present study aimed at providing a solution by the means of a well-calibrated crop model. A comprehensive dataset from a two year experiment on subsurface drip irrigated and N-fertigated potato was used to calibrate and validate the Daisy model with focus on potato crop parameters. The experiment included two irrigation regimes combined with five different N fertigation levels. The calibrated model simulated LAI evolution, crop growth, N uptake and final tuber dry matter and N yield satisfactorily with discrepancies between the simulated and measured variables when simulations were evaluated treatment by treatment. The root mean squared error (RMSE) of the simulated tuber dry matter yield were 0.57 and 0.69 t DM ha-1 for the Folva cultivar in 2013 and Sava cultivar in 2014, respectively. For tuber nitrogen yield, RMSE of the model simulations were 9.7 and 12.8 kg N ha-1 in 2013 and 2014, respectively. Results demonstrated that the calibrated Daisy model could be used as a DSS tool for the optimal irrigation and fertigation of Folva and Sava potato cultivars. Compared to the experimental data the same tuber dry matter yield was simulated with substantial savings 30–55% and 35–50% of irrigation water and N fertiliser, respectively. Results indicated an overestimation of bare soil evaporation, and inconsistences in the simulated effects of water and N stresses on the crop. • Modelling using comprehensive data set on drip irrigated and N fertigated potato. • Calibrated Daisy model satisfactorily simulated potato yield and nitrogen uptake. • Model guided drip irrigation and nitrogen fertigation management significantly save irrigation water and nitrogen fertiliser. [ABSTRACT FROM AUTHOR]