Special Issue "Digital Innovations in Agriculture"

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 20 February 2022.

Special Issue Editors

Prof. Dr. Gniewko Niedbała
E-Mail Website
Guest Editor
Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
Interests: artificial neural networks; artificial intelligence; machine learning; yield modelling; predictions; forecasting; crop production
Special Issues, Collections and Topics in MDPI journals
Dr. Sebastian Kujawa
E-Mail Website
Guest Editor
Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50 Street, 60-627 Poznań, Poland
Interests: artificial neural networks; artificial intelligence; machine learning; yield modelling; predictions; forecasting; crop production
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

World population is increasing significantly and is expected to reach almost 10 billion in the year 2050. At the same time, observed climate change is accelerating and affecting the agricultural production strongly. These aspects, as well as the latest socio-economic limitations caused by the Covid-19 pandemic, bring new challenges to modern agriculture and the need to have high production efficiency combined with a high quality of obtained products in accordance with the principles of sustainable production. This applies to both crop and livestock production, as well as the other domains related to food production.

To meet these challenges, advanced digital innovation techniques are more and more frequently being used, including those based on machine learning, artificial neural networks, Internet of Things (IoT) and big data. They are widely applied in solving various optimization tasks in the agri-food production processes in the context of the increasing use of precision and digital farming technologies on the path from Agriculture 3.0 to 5.0.

We invite authors to submit all types of manuscripts, including original research, research concepts, communications, and reviews related to digital innovation, widely defined, in the agri-food sector.

Prof. Dr. Gniewko Niedbała
Dr. Sebastian Kujawa
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digital farming
  • precision agriculture
  • machine learning
  • artificial neural networks
  • Internet of Things (IoT)
  • Big data
  • image processing & analysis
  • proximal and remote sensing
  • data analysis and decision support
  • agricultural information systems (FMIS, ERP)
  • traceability
  • other digital innovations in agriculture

Published Papers (13 papers)

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Research

Article
Modeling the Essential Oil and Trans-Anethole Yield of Fennel (Foeniculum vulgare Mill. var. vulgare) by Application Artificial Neural Network and Multiple Linear Regression Methods
Agriculture 2021, 11(12), 1191; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11121191 - 26 Nov 2021
Viewed by 267
Abstract
Foeniculum vulgare Mill. (commonly known as fennel) is used in the pharmaceutical, cosmetic, and food industries. Fennel widely used as a digestive, carminative, galactagogue and diuretic and in treating gastrointestinal and respiratory disorders. Improving low heritability traits such as essential oil yield (EOY%) [...] Read more.
Foeniculum vulgare Mill. (commonly known as fennel) is used in the pharmaceutical, cosmetic, and food industries. Fennel widely used as a digestive, carminative, galactagogue and diuretic and in treating gastrointestinal and respiratory disorders. Improving low heritability traits such as essential oil yield (EOY%) and trans-anethole yield (TAY%) of fennel by direct selection does not result in rapid gains of EOY% and TAY%. Identification of high-heritable traits and using efficient modeling methods can be a beneficial approach to overcome this limitation and help breeders select the most advantageous traits in medicinal plant breeding programs. The present study aims to compare the performance of the artificial neural network (ANN) and multilinear regression (MLR) to predict the EOY% and TAY% of fennel populations. Stepwise regression (SWR) was used to assess the effect of various input variables. Based on SWR, nine traits—number of days to 50% flowering (NDF50%), number of days to maturity (NDM), final plant height (FPH), number of internodes (NI), number of umbels (NU), seed yield per square meter (SY/m2), number of seeds per plant (NS/P), number of seeds per umbel (NS/U) and 1000-seed weight (TSW)—were chosen as input variables. The network with Sigmoid Axon transfer function and two hidden layers was selected as the final ANN model for the prediction of EOY%, and the TanhAxon function with one hidden layer was used for the prediction of TAY%. The results revealed that the ANN method could predict the EOY% and TAY% with more accuracy and efficiency (R2 of EOY% = 0.929, R2 of TAY% = 0.777, RMSE of EOY% = 0.544, RMSE of TAY% = 0.264, MAE of EOY% = 0.385 and MAE of TAY% = 0.352) compared with the MLR model (R2 of EOY% = 0.553, R2 of TAY% = 0.467, RMSE of EOY% = 0.819, RMSE of TAY% = 0.448, MAE of EOY% = 0.624 and MAE of TAY% = 0.452). Based on the sensitivity analysis, SY/m2, NDF50% and NS/P were the most important traits to predict EOY% as well as SY/m2, NS/U and NDM to predict of TAY%. The results demonstrate the potential of ANNs as a promising tool to predict the EOY% and TAY% of fennel, and they can be used in future fennel breeding programs. Full article
(This article belongs to the Special Issue Digital Innovations in Agriculture)
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Article
Estimation of Soil Nutrient Content Using Hyperspectral Data
Agriculture 2021, 11(11), 1129; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11111129 - 11 Nov 2021
Viewed by 326
Abstract
Soil nutrients play a vital role in plant growth and thus the rapid acquisition of soil nutrient content is of great significance for agricultural sustainable development. Hyperspectral remote-sensing techniques allow for the quick monitoring of soil nutrients. However, at present, obtaining accurate estimates [...] Read more.
Soil nutrients play a vital role in plant growth and thus the rapid acquisition of soil nutrient content is of great significance for agricultural sustainable development. Hyperspectral remote-sensing techniques allow for the quick monitoring of soil nutrients. However, at present, obtaining accurate estimates proves to be difficult due to the weak spectral features of soil nutrients and the low accuracy of soil nutrient estimation models. This study proposed a new method to improve soil nutrient estimation. Firstly, for obtaining characteristic variables, we employed partial least squares regression (PLSR) fit degree to select an optimal screening algorithm from three algorithms (Pearson correlation coefficient, PCC; least absolute shrinkage and selection operator, LASSO; and gradient boosting decision tree, GBDT). Secondly, linear (multi-linear regression, MLR; ridge regression, RR) and nonlinear (support vector machine, SVM; and back propagation neural network with genetic algorithm optimization, GABP) algorithms with 10-fold cross-validation were implemented to determine the most accurate model for estimating soil total nitrogen (TN), total phosphorus (TP), and total potassium (TK) contents. Finally, the new method was used to map the soil TK content at a regional scale using the soil component spectral variables retrieved by the fully constrained least squares (FCLS) method based on an image from the HuanJing-1A Hyperspectral Imager (HJ-1A HSI) of the Conghua District of Guangzhou, China. The results identified the GBDT-GABP was observed as the most accurate estimation method of soil TN ( of 0.69, the root mean square error of cross-validation (RMSECV) of 0.35 g kg−1 and ratio of performance to interquartile range (RPIQ) of 2.03) and TP ( of 0.73, RMSECV of 0.30 g kg−1 and RPIQ = 2.10), and the LASSO-GABP proved to be optimal for soil TK estimations ( of 0.82, RMSECV of 3.39 g kg−1 and RPIQ = 3.57). Additionally, the highly accurate LASSO-GABP-estimated soil TK (R2 = 0.79) reveals the feasibility of the LASSO-GABP method to retrieve soil TK content at the regional scale. Full article
(This article belongs to the Special Issue Digital Innovations in Agriculture)
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Article
Simplified and Hybrid Remote Sensing-Based Delineation of Management Zones for Nitrogen Variable Rate Application in Wheat
Agriculture 2021, 11(11), 1104; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11111104 - 05 Nov 2021
Viewed by 443
Abstract
Enhancing digital and precision agriculture is currently inevitable to overcome the economic and environmental challenges of the agriculture in the 21st century. The purpose of this study was to generate and compare management zones (MZ) based on the Sentinel-2 satellite data for variable [...] Read more.
Enhancing digital and precision agriculture is currently inevitable to overcome the economic and environmental challenges of the agriculture in the 21st century. The purpose of this study was to generate and compare management zones (MZ) based on the Sentinel-2 satellite data for variable rate application of mineral nitrogen in wheat production, calculated using different remote sensing (RS)-based models under varied soil, yield and crop data availability. Three models were applied, including (1) a modified “RS- and threshold-based clustering”, (2) a “hybrid-based, unsupervised clustering”, in which data from different sources were combined for MZ delineation, and (3) a “RS-based, unsupervised clustering”. Various data processing methods including machine learning were used in the model development. Statistical tests such as the Paired Sample T-test, Kruskal–Wallis H-test and Wilcoxon signed-rank test were applied to evaluate the final delineated MZ maps. Additionally, a procedure for improving models based on information about phenological phases and the occurrence of agricultural drought was implemented. The results showed that information on agronomy and climate enables improving and optimizing MZ delineation. The integration of prior knowledge on new climate conditions (drought) in image selection was tested for effective use of the models. Lack of this information led to the infeasibility of obtaining optimal results. Models that solely rely on remote sensing information are comparatively less expensive than hybrid models. Additionally, remote sensing-based models enable delineating MZ for fertilizer recommendations that are temporally closer to fertilization times. Full article
(This article belongs to the Special Issue Digital Innovations in Agriculture)
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Article
Evaluation of Deep Learning for Automatic Multi-View Face Detection in Cattle
Agriculture 2021, 11(11), 1062; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11111062 - 28 Oct 2021
Cited by 1 | Viewed by 445
Abstract
Individual identification plays an important part in disease prevention and control, traceability of meat products, and improvement of agricultural false insurance claims. Automatic and accurate detection of cattle face is prior to individual identification and facial expression recognition based on image analysis technology. [...] Read more.
Individual identification plays an important part in disease prevention and control, traceability of meat products, and improvement of agricultural false insurance claims. Automatic and accurate detection of cattle face is prior to individual identification and facial expression recognition based on image analysis technology. This paper evaluated the possibility of the cutting-edge object detection algorithm, RetinaNet, performing multi-view cattle face detection in housing farms with fluctuating illumination, overlapping, and occlusion. Seven different pretrained CNN models (ResNet 50, ResNet 101, ResNet 152, VGG 16, VGG 19, Densenet 121 and Densenet 169) were fine-tuned by transfer learning and re-trained on the dataset in the paper. Experimental results showed that RetinaNet incorporating the ResNet 50 was superior in accuracy and speed through performance evaluation, which yielded an average precision score of 99.8% and an average processing time of 0.0438 s per image. Compared with the typical competing algorithms, the proposed method was preferable for cattle face detection, especially in particularly challenging scenarios. This research work demonstrated the potential of artificial intelligence towards the incorporation of computer vision systems for individual identification and other animal welfare improvements. Full article
(This article belongs to the Special Issue Digital Innovations in Agriculture)
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Article
Estimation and Forecasting of Rice Yield Using Phenology-Based Algorithm and Linear Regression Model on Sentinel-II Satellite Data
Agriculture 2021, 11(10), 1026; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11101026 - 19 Oct 2021
Viewed by 502
Abstract
Rice is a primary food for more than three billion people worldwide and cultivated on about 12% of the world’s arable land. However, more than 88% production is observed in Asian countries, including Pakistan. Due to higher population growth and recent climate change [...] Read more.
Rice is a primary food for more than three billion people worldwide and cultivated on about 12% of the world’s arable land. However, more than 88% production is observed in Asian countries, including Pakistan. Due to higher population growth and recent climate change scenarios, it is crucial to get timely and accurate rice yield estimates and production forecast of the growing season for governments, planners, and decision makers in formulating policies regarding import/export in the event of shortfall and/or surplus. This study aims to quantify the rice yield at various phenological stages from hyper-temporal satellite-derived-vegetation indices computed from time series Sentinel-II images. Different vegetation indices (viz. NDVI, EVI, SAVI, and REP) were used to predict paddy yield. The predicted yield was validated through RMSE and ME statistical techniques. The integration of PLSR and sequential time-stamped vegetation indices accurately predicted rice yield (i.e., maximum R2 = 0.84 and minimum RMSE = 0.12 ton ha−1 equal to 3% of the mean rice yield). Moreover, our results also established that optimal time spans for predicting rice yield are late vegetative and reproductive (flowering) stages. The output would be useful for the farmer and decision makers in addressing food security. Full article
(This article belongs to the Special Issue Digital Innovations in Agriculture)
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Article
Lettuce Growth Pattern Analysis Using U-Net Pre-Trained with Arabidopsis
Agriculture 2021, 11(9), 890; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11090890 - 16 Sep 2021
Viewed by 766
Abstract
To overcome the challenges related to food security, digital farming has been proposed, wherein the status of a plant using various sensors could be determined in real time. The high-throughput phenotyping platform (HTPP) and analysis with deep learning (DL) are increasingly being used [...] Read more.
To overcome the challenges related to food security, digital farming has been proposed, wherein the status of a plant using various sensors could be determined in real time. The high-throughput phenotyping platform (HTPP) and analysis with deep learning (DL) are increasingly being used but require a lot of resources. For botanists who have no prior knowledge of DL, the image analysis method is relatively easy to use. Hence, we aimed to explore a pre-trained Arabidopsis DL model to extract the projected area (PA) for lettuce growth pattern analysis. The accuracies of the extract PA of the lettuce cultivar “Nul-chung” with a pre-trained model was measured using the Jaccard Index, and the median value was 0.88 and 0.87 in two environments. Moreover, the growth pattern of green lettuce showed reproducible results in the same environment (p < 0.05). The pre-trained model successfully extracted the time-series PA of lettuce under two lighting conditions (p < 0.05), showing the potential application of a pre-trained DL model of target species in the study of traits in non-target species under various environmental conditions. Botanists and farmers would benefit from fewer challenges when applying up-to-date DL in crop analysis when few resources are available for image analysis of a target crop. Full article
(This article belongs to the Special Issue Digital Innovations in Agriculture)
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Article
Impact of Lameness on Attributes of Feeding Registered with Noseband Sensor in Fresh Dairy Cows
Agriculture 2021, 11(9), 851; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11090851 - 06 Sep 2021
Viewed by 728
Abstract
We hypothesized that lameness in fresh dairy cows (1–30 days after calving) has an impact on attributes of feeding registered with a noseband sensor. The aim of this study was to investigate the impact of lameness in fresh dairy cows on attributes of [...] Read more.
We hypothesized that lameness in fresh dairy cows (1–30 days after calving) has an impact on attributes of feeding registered with a noseband sensor. The aim of this study was to investigate the impact of lameness in fresh dairy cows on attributes of feeding (registered with the RumiWatch noseband sensor): rumination time (RT), drinking time (DT), eating time (ET), rumination chews (RC), eating chews (EC), chews per minute (CM), drinking gulps (DG), bolus count (B), and chews per bolus (CB). The measurement registration was started at the first day after calving and continued until 30 days after calving. There were 20 Lithuanian black and white breed cows selected. Lameness diagnosis was performed by trained staff based on a locomotion score system and it was diagnosed on average on the 15th day after calving. The causes of lameness were categorized as sole ulcer, abscess and foot rot. Special attention was paid to attributes of feeding registered 14 days before and 13 days after diagnosis. The 10 lame cows (LG) used in this experiment had a lameness score of 3–4 presented with severe lameness: they were reluctant to move and unwilling to complete weight transfer off the affected limb. The 10 healthy cows (HG) were given a lameness score of 1. We found that lameness of fresh dairy cows has an impact on inline registered ingestive behaviors biomarkers—the mean RT of HG cows was as much as 2.19 times higher than that of LG cows on the day of diagnosis of lameness, later this difference between the groups decreased to the sixth day of treatment, then increased again and decreased at the end of the experiment. The lowest eating time was found on diagnosis day and the highest on the ninth day before determination of lameness. Drinking time was higher in the HG group, with the exception of 10 and 9 days prior to clinical signs of disease in LG cows. A downward trend in rumination chews was observed in LG cows from day 7 until the onset of clinical symptoms. The bolus count decreased from day 3 before diagnosis to day 1 after diagnosis in LG cows. The largest difference in this indicator between groups was found on day of diagnosis. Analysing the pattern of CM values in the LG group, we found a decrease from 10 days before to 2 days after diagnosis. The CB value was almost the same in both groups of cows at the end of the experiment, but largest difference between the groups was found on day 7 after clinical sings of lameness. Full article
(This article belongs to the Special Issue Digital Innovations in Agriculture)
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Article
Identification of Risk Factors for Lameness Detection with Help of Biosensors
Agriculture 2021, 11(7), 610; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11070610 - 29 Jun 2021
Viewed by 534
Abstract
In this study we hypothesized that the lameness of early lactation dairy cows would have an impact on inline biomarkers, such as rumination time (RT), milk fat (%), milk protein (%), milk fat/protein ratio (F/P), milk lactose (L, %), milk electrical conductivity of [...] Read more.
In this study we hypothesized that the lameness of early lactation dairy cows would have an impact on inline biomarkers, such as rumination time (RT), milk fat (%), milk protein (%), milk fat/protein ratio (F/P), milk lactose (L, %), milk electrical conductivity of all udder quarters, body weight (BW), temperature of reticulorumen content (TRR), pH of reticulorumen content (pH), and walking activity (activity). All 30 lame cows (LCs) used in this experiment had a score of 3–4, identified according to the standard procedure of Sprecher et al. The 30 healthy cows (HC) showed a lameness score of one. RT, milk fat, MY, milk protein, F/P, L, milk electrical conductivity of all udder quarters, and BW were registered using Lely Astronaut® A3 milking robots each time the cow was being milked. The TRR, cow activity, and pH of the contents of each cow’s reticulorumen were registered using specific smaXtec boluses. The study lasted a total of 28 days. Days “−14” to “−1” denote the days of the experimental period before the onset of clinical signs of lameness (day “0”), and days “1” to “13” indicate the period after the start of treatment. We found that from the ninth day before the diagnosis of laminitis until the end of our study, LCs had higher milk electrical conductivity in all udder quarters, and higher milk fat to protein ratios. On the 3rd day before the onset of clinical signs of the disease until the day of diagnosis, the milk fat of the LC group was reduced. The activity of the LCs decreased sharply from the second day to the first day after treatment. RT in the HC group tended to decrease during the experiment. pH in LCs also increased on the day of the appearance of clinical signs. Full article
(This article belongs to the Special Issue Digital Innovations in Agriculture)
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Article
Automated Chicken Counting in Surveillance Camera Environments Based on the Point Supervision Algorithm: LC-DenseFCN
Agriculture 2021, 11(6), 493; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11060493 - 26 May 2021
Viewed by 1056
Abstract
The density of a chicken population has a great influence on the health and growth of the chickens. For free-range chicken producers, an appropriate population density can increase their economic benefit and be utilized for estimating the economic value of the flock. However, [...] Read more.
The density of a chicken population has a great influence on the health and growth of the chickens. For free-range chicken producers, an appropriate population density can increase their economic benefit and be utilized for estimating the economic value of the flock. However, it is very difficult to calculate the density of chickens quickly and accurately because of the complicated environmental background and the dynamic number of chickens. Therefore, we propose an automated method for quickly and accurately counting the number of chickens on a chicken farm, rather than doing so manually. The contributions of this paper are twofold: (1) we innovatively designed a full convolutional network—DenseFCN—and counted the chickens in an image using the method of point supervision, which achieved an accuracy of 93.84% and 9.27 frames per second (FPS); (2) the point supervision method was used to detect the density of chickens. Compared with the current mainstream object detection method, the higher effectiveness of this method was proven. From the performance evaluation of the algorithm, the proposed method is practical for measuring the density statistics of chickens in a farm environment and provides a new feasible tool for the density estimation of farm poultry breeding. Full article
(This article belongs to the Special Issue Digital Innovations in Agriculture)
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Article
Extraction of Arecanut Planting Distribution Based on the Feature Space Optimization of PlanetScope Imagery
Agriculture 2021, 11(4), 371; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11040371 - 19 Apr 2021
Cited by 2 | Viewed by 614
Abstract
The remote sensing extraction of large areas of arecanut (Areca catechu L.) planting plays an important role in investigating the distribution of arecanut planting area and the subsequent adjustment and optimization of regional planting structures. Satellite imagery has previously been used to [...] Read more.
The remote sensing extraction of large areas of arecanut (Areca catechu L.) planting plays an important role in investigating the distribution of arecanut planting area and the subsequent adjustment and optimization of regional planting structures. Satellite imagery has previously been used to investigate and monitor the agricultural and forestry vegetation in Hainan. However, the monitoring accuracy is affected by the cloudy and rainy climate of this region, as well as the high level of land fragmentation. In this paper, we used PlanetScope imagery at a 3 m spatial resolution over the Hainan arecanut planting area to investigate the high-precision extraction of the arecanut planting distribution based on feature space optimization. First, spectral and textural feature variables were selected to form the initial feature space, followed by the implementation of the random forest algorithm to optimize the feature space. Arecanut planting area extraction models based on the support vector machine (SVM), BP neural network (BPNN), and random forest (RF) classification algorithms were then constructed. The overall classification accuracies of the SVM, BPNN, and RF models optimized by the RF features were determined as 74.82%, 83.67%, and 88.30%, with Kappa coefficients of 0.680, 0.795, and 0.853, respectively. The RF model with optimized features exhibited the highest overall classification accuracy and kappa coefficient. The overall accuracy of the SVM, BPNN, and RF models following feature optimization was improved by 3.90%, 7.77%, and 7.45%, respectively, compared with the corresponding unoptimized classification model. The kappa coefficient also improved. The results demonstrate the ability of PlanetScope satellite imagery to extract the planting distribution of arecanut. Furthermore, the RF is proven to effectively optimize the initial feature space, composed of spectral and textural feature variables, further improving the extraction accuracy of the arecanut planting distribution. This work can act as a theoretical and technical reference for the agricultural and forestry industries. Full article
(This article belongs to the Special Issue Digital Innovations in Agriculture)
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Article
Prediction of Reproductive Success in Multiparous First Service Dairy Cows by Parameters from In-Line Sensors
Agriculture 2021, 11(4), 334; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11040334 - 08 Apr 2021
Viewed by 592
Abstract
The aim of the current study was to evaluate the relationship of different parameters from an automatic milking system (AMS) with the pregnancy status of multiparous cows at first service and to assess the accuracy of such a follow-up with regard to blood [...] Read more.
The aim of the current study was to evaluate the relationship of different parameters from an automatic milking system (AMS) with the pregnancy status of multiparous cows at first service and to assess the accuracy of such a follow-up with regard to blood parameters. Before the insemination of cows, blood samples for measuring biochemical indices were taken from the coccygeal vessels and the concentrations of blood serum albumin (ALB), cortisol, non-esterified fatty acids (NEFA) and the activities of aspartate aminotransferase (AST) and gamma glutamyltransferase (GGT) were determined. From oestrus day to seven days after oestrus, the following parameters were registered: milk yield (MY), electric milk conductivity, lactate dehydrogenase (LDH) and β-hydroxybutyric acid (BHB). The pregnancy status was evaluated using ultrasound “Easy scan” 30–35 days after insemination. Cows were grouped by reproductive status: PG− (non-pregnant; n = 48) and PG+ (pregnant; n = 44). The BHB level in PG− cows was 1.2 times higher (p < 0.005). The electrical conductivity of milk was statistically significantly higher in all quarters of PG− cows (1.07 times) than of PG+ cows (p < 0.05). The arithmetic mean of blood GGT was 1.61 times higher in PG− cows and the NEFA value 1.23 times higher (p < 0.05) compared with the PG+ group. The liver function was affected, the average ALB of PG− cows was 1.19 times lower (p < 0.05) and the AST activity was 1.16 times lower (p < 0.05) compared with PG+ cows. The non-pregnant group had a negative energy balance demonstrated by high in-line milk BHB and high blood NEFA concentrations. We found a greater number of cows with cortisol >0.0.75 mg/dL in the non-pregnant group. A higher milk electrical conductivity in the non-pregnant cows pointed towards a greater risk of mastitis while higher GGT activities together with lower albumin concentrations indicated that the cows were more affected by oxidative stress. Full article
(This article belongs to the Special Issue Digital Innovations in Agriculture)
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Article
Simulation of Fuel Consumption Based on Engine Load Level of a 95 kW Partial Power-Shift Transmission Tractor
Agriculture 2021, 11(3), 276; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11030276 - 23 Mar 2021
Cited by 1 | Viewed by 1064
Abstract
This study is focused on the estimation of fuel consumption of the power-shift transmission (PST) tractor based on PTO (power take-off) dynamometer test. The simulation model of PST tractor was developed using the configurations and powertrain of the real PST tractor. The PTO [...] Read more.
This study is focused on the estimation of fuel consumption of the power-shift transmission (PST) tractor based on PTO (power take-off) dynamometer test. The simulation model of PST tractor was developed using the configurations and powertrain of the real PST tractor. The PTO dynamometer was installed to measure the engine load and fuel consumption at various engine load levels (40, 50, 60, 70, 80, and 90%), and verify the simulation model. The axle load was also predicted using tractor’s specifications as an input parameter of the simulation model. The simulation and measured results were analyzed and compared statistically. It was observed that the engine load, as well as fuel consumption, were directly proportional to the engine load levels. However, it was statistically proved that there was no significant difference between the simulation and measured engine torque and fuel consumption at each load level. The regression equations show that there was an exponential relationship between the fuel consumption and engine load levels. However, the specific fuel consumptions (SFC) for both simulation and measured were linear relationships and had no significant difference between them at each engine load level. The results were statistically proved that the simulation and measured SFCs were similar trends. The plow tillage operation could be performed at the gear stage of 7.65 km/h with higher working efficiency at low fuel consumption. The drawback of this study is to use a constant axle load instead of dynamic load. This study can provide useful information for both researchers and manufacturers related to the automated transmission of an agricultural tractor, especially PST tractor for digital farming solutions. Finally, it could contribute to the manufacturers developing a new agricultural tractor with higher fuel efficiency. Full article
(This article belongs to the Special Issue Digital Innovations in Agriculture)
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Article
Can Milk Flow Traits Act as Biomarkers of Lameness in Dairy Cows?
Agriculture 2021, 11(3), 227; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11030227 - 09 Mar 2021
Viewed by 557
Abstract
We hypothesized that lameness has an impact on milk flow traits. The aim of the current study was therefore to investigate the relation between lameness and milk flow traits in dairy cows. For this study 73 healthy and 55 cows with lameness were [...] Read more.
We hypothesized that lameness has an impact on milk flow traits. The aim of the current study was therefore to investigate the relation between lameness and milk flow traits in dairy cows. For this study 73 healthy and 55 cows with lameness were selected. Lameness was diagnosed by a local specialized veterinarian, according to the standard procedure. The blood samples were collected during clinical examination. The milking properties of cows were evaluated twice in a row—during evening and morning milking. The selected cows in the current lactation did not receive veterinary treatment, and correct hoof trimming was not performed at least four weeks before the experiment. The measurements were taken by two electronic mobile milk flow meters (Lactocorder®®, WMB AG, Balgache, Switzerland). Milk flow data were processed using LactoPro 5.2.0 software (Biomelktechnik Swiss). Cortisol concentration was measured with the automated analyzer TOSOH®® AIA-360 (South San Francisco, CA, USA). We found out that milk flow traits can act as biomarkers of lameness in dairy cows. We determined that the milk yield in the first minute of healthy dairy cows was 1.77 kg higher than that of lame cows. The electrical conductivity during the initial time of milking of healthy cows was 0.24 mS/cm lower than that of the lame group. The milking duration of LA cows was 1.07 min shorter and the time of incline in milk flow from 0.5 kg/min till the reach of the plateau phase was longer. The risk of lameness was most clearly indicated by an increase in blood cortisol concentration; if its blood level in cows exceeds 1 µg/dL, the risk of identifying lameness increases 4.9 times. Full article
(This article belongs to the Special Issue Digital Innovations in Agriculture)
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