Allele frequencies of κ-CN, β-CN, αs1-CN, αs2-CN and β-LG in cows of the Estonian Holstein breed
\r\n\tPrevalence of reading disability among school-age children depends upon the criteria used for definition; however, the prevalence of written expression disorders in estimated to be between 5 and 12 percent, the prevalence of written expression disorders is estimated to be between 7 and 15 percent, while the prevalence of dyscalculia is estimated to be between 3 and 6 percent.
\r\n\r\n\tRisk factors for learning disorders are family history, socio-economic conditions, prematurity, presence of other developmental, mental and health conditions (e.g. behavioral disorders, autism, attention deficit and hyperactivity disorders), prenatal exposition to neurotoxic agents, genetic disorders, particular medical conditions, history of traumatic brain injury or other neurological conditions.
",isbn:"978-1-83968-588-0",printIsbn:"978-1-83968-587-3",pdfIsbn:"978-1-83968-589-7",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"0999e5f759c2380ae5a4a2ee0835c98d",bookSignature:" Sandro Misciagna",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10910.jpg",keywords:"Learning Disability Definition, Brain Plasticity, Learning Disability Evaluation, Learning Disabilities Resources, Psychoeducation Evaluation, Clinical Features, Dyslexia, Dysgraphia, Dyscalculia, Intellectual Disabilities, Autism Spectrum Disorders, ADHD",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 16th 2021",dateEndSecondStepPublish:"May 14th 2021",dateEndThirdStepPublish:"July 13th 2021",dateEndFourthStepPublish:"October 1st 2021",dateEndFifthStepPublish:"November 30th 2021",remainingDaysToSecondStep:"25 days",secondStepPassed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:"Dr. Sandro Misciagna received his degree in medicine at the Catholic University in Rome. As a clinician, he has worked in different neurological departments in Italian hospitals, Alzheimer’s clinics, neuropsychiatric clinics, and neurological rehabilitative departments as the Neurological Department and Stroke Unit of Belcolle Hospital in Viterbo, Italy.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"103586",title:null,name:"Sandro",middleName:null,surname:"Misciagna",slug:"sandro-misciagna",fullName:"Sandro Misciagna",profilePictureURL:"https://mts.intechopen.com/storage/users/103586/images/system/103586.jpg",biography:"Dr. Sandro Misciagna was born in Italy in 1969. He received a degree in medicine in 1995 and another in neurology in 1999 from The Catholic University, Rome. From 1993 to 1995, he was involved in research of cerebellar functions. From 1994 to 2003, he attended the Neuropsychological department involved in research in cognitive and behavioural disorders. From 2001 to 2003, he taught neuropsychology, neurology, and cognitive rehabilitation. In 2003, he obtained a Ph.D. in Neuroscience with a thesis on the behavioural and cognitive profile of frontotemporal dementia. Dr. Misciagna has worked in various neurology departments, Alzheimer’s clinics, neuropsychiatric clinics, and neuro-rehabilitative departments. In November 2016, he began working as a neurologist at Belcolle Hospital, Viterbo, where he has run the epilepsy centre since February 2019.",institutionString:"Ospedale di Belcolle",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"Ospedale di Belcolle",institutionURL:null,country:{name:"Italy"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"21",title:"Psychology",slug:"psychology"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"280415",firstName:"Josip",lastName:"Knapic",middleName:null,title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/280415/images/8050_n.jpg",email:"josip@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copy-editing and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2270",title:"Fourier Transform",subtitle:"Materials Analysis",isOpenForSubmission:!1,hash:"5e094b066da527193e878e160b4772af",slug:"fourier-transform-materials-analysis",bookSignature:"Salih Mohammed Salih",coverURL:"https://cdn.intechopen.com/books/images_new/2270.jpg",editedByType:"Edited by",editors:[{id:"111691",title:"Dr.Ing.",name:"Salih",surname:"Salih",slug:"salih-salih",fullName:"Salih Salih"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"39312",title:"Milk Protein Genotype Associations with Milk Coagulation and Quality Traits",doi:"10.5772/29123",slug:"milk-protein-genotype-associations-with-milk-coagulation-and-quality-traits",body:'Cheese production is of substantial economic importance in most European countries, where an increasing amount of the produced milk is used for manufacturing cheese (Eurostat 2010a). In Estonia 60% (Statistics Estonia, 2010), in Italy more than 75% (De Marchi et al., 2008), and in Scandinavian countries 33% (Wedholm et al., 2006) of milk is used for cheese production. Recent trends indicate that per capita consumption of cheese is also increasing (Eurostat 2010b).
Milk quality is an essential factor to the dairy industry due to its economic impact. Milk coagulation ability is one of the most important factors affecting cheese yield and quality and has been reviewed (Jakob & Puhan, 1992; Johnson et al., 2001), and therefore is becoming more important as an increasing percentage of milk is used for cheese manufacturing. Milk coagulation properties (MCP) are commonly defined by milk coagulation time (RCT) and curd firmness (A30). It is feasible to design raw milk according to its specific technological use.
Improving cheese yield and quality, through the direct selection of breeding animals on the basis of milk coagulation property traits, is an option due to genetic variation of MCP traits (Ojala et al., 2005). MCP are heritable, quantitative traits; up to 40% of the variation among animals is caused by genetic factors (Ikonen et al., 2004). Estimates of heritability for MCP traits are from 0.30 to 0.40 (Bittante et al., 2002; Ikonen et al., 1999a), and from 0.25 for RCT, and 0.15 for A30 (Cassandro et al., 2008) to 0.28 for RCT to 0.41 for A30 (Vallas et al., 2010). Predictions of MCP provided by mid-infrared spectroscopy (MIR) techniques have been proposed as indicator traits for the genetic enhancement of MCP (Cecchinato et al., 2009; De Marchi et al., 2009). The expected response of RCT and A30 ensured by the selection using MIR predictions as indicator traits was equal to, or slightly less than, the response achievable through a single measurement of these traits. Accordingly, breeding strategies for the enhancement of MCP based on MIR predictions as indicator traits could be easily and immediately implemented for dairy cattle populations where the routine acquisition of spectra from individual milk samples is already measured (Cecchinato et al., 2009). Nevertheless, MCP traits analyzed with different methodologies have significantly different values, due to the diversity of the instruments used and the coagulant activity (Pretto et al., 2011). The type of coagulant could also have an effect, since different coagulants have been used. The method proposed for the prediction of non-coagulation probability of milk samples showed that non coagulating samples from one methodology were highly predictable based on the rennet coagulation time measured with another methodology (Pretto et al., 2011). A standard definition of MCP traits analysis is needed to enable reliable comparisons between MCP traits recorded in different laboratories, and in different animal populations and breeds.
More than 95% of the proteins contained in ruminant milk are coded by six structural genes (Martin et al., 2002). The four casein genes (
In the Dutch Holstein-Friesian population selection for
The objective of this study was to estimate the contribution of the aggregate β-κ-CN and β-LG genotypes on first lactation milk coagulation and quality traits of Estonian Holstein cows. A parallel objective was to identify the variation in genetic polymorphism of milk proteins with the aim to improve the protein composition in milk by selecting for variants of specific genes.
In Estonia there are three breeds of dairy cattle – Estonian Red (ER), Estonian Holstein (EHF) and Estonian Native (EN). Distribution of breeds has come to favour EHF (Fig.1). In Estonia, 93.0% of cows are enrolled in an official milk recording programme (Results of Animal Recording in Estonia 2010, 2011) (Fig. 2). Since 1995, average milk yield in Estonia has risen 3947 kg (48%, Fig. 3). The 2010 average actual production for Estonian Holstein herd that were enrolled in producing-testing programs and eligible for genetic evaluation was 7778 kg milk, 317 kg of fat and 260 kg of protein per year. Holstein dairy cattle dominate in Estonian milk production industry because of their excellent production and greater income.
Changes over time in number of cows of each indicated breed on a milk recording programme
Changes over time in number of Estonian dairy cows and the proportion on a milk recording programme
In the evaluation programme for young bulls of Estonian Holstein breed, ca 25 bulls are tested each year, parallel testing is carried out on 10-12 foreign bulls. The selection of bulls is made from imported American and Canadian embryos, the Estonian Holstein best bull dams and imported young bulls. Often the sons of imported cows are used whose sires are world-known top bulls. The bulls come mainly from the USA, Canada, Germany and the Netherlands.
Currently, estimated breeding values (EBV) for production, conformation and udder health traits for bulls and cows in Estonia are computed by the Animal Recording Centre four times per year (Pentjärv & Uba, 2004). Breeding value estimation is carried out separately for the EHF and the Estonian Red breed (ER), using the best linear unbiased predictor (BLUP) test day animal model for production and udder health traits and the BLUP animal model for conformation traits. The EBV for each production trait – milk (kg), fat (kg) and protein (kg) – is the mean breeding value of the first, second and third lactations, adjusted by the mean average breeding value of the cows born in a defined base year (currently, 1995).
The milk production index (SPAV) is expressed as relative breeding value (RBV) with a mean of 100 and a standard deviation of 12 points for base animals, combining breeding values for milk, fat and protein yield weighted by relative economic values of 0:1:4 for EHF and 0:1:6 for ER (Pentjärv & Uba, 2004).
Changes over time in annual milk yield per cow of each indicated breed
The information source for breeding value estimation of udder health traits is somatic cell count (SCC) in one millilitre of milk, transformed into a somatic cell score (SCS) using the formula SCS = log2 (SCC/100000) + 3 (Pentjärv & Uba, 2004).
The udder health index SSAV is calculated as the sum of EBVs of the first, second and third lactations with index weights 0.26, 0.37 and 0.37, respectively, and is expressed as RBV for genetic evaluation of conformation traits. Data from first lactation cows are used to compute RBVs for 16 linear traits for EHF and 14 linear traits for ER, as well as for three general traits. The conformation index SVAV is expressed as RBV, combining relative breeding values for type, udder and feet by relative economic weights of 0.3:0.5:0.2 for ER and 0.3:0.4:0.3 for EHF.
First lactation milk samples were collected during routine milk recording as part of a development project for the Bio-Competence Centre of Healthy Dairy Products in Estonia during the period April 2005 – June 2008. The herds had twice-a-day or thrice-a-day milkings. The individual milk samples collected from the cows were either bulked test-day milkings or separate samples from each of the milkings on the test-day. Milk samples were immediately preserved after collection with Bronopol (2-bromo-2-nitropropane-1,3-diol, Knoll Pharmaceuticals, Nottingham, UK) and stored at 4˚C during transportation and analyzing periods. Milk samples with a pH lower than 6.5, indicative of colostrum, and non-coagulated milk samples (n=33) were excluded from the analysis. Furthermore, farms with less than 10 cows, and cows with fewer than three test-day records, were removed. The final dataset used for analyses consist of 11,437 test-day records from 2,769 Estonian Holstein cows which were located in 66 herds across the country and were daughters of 169 sires. The number of daughters per sire ranged from 1 to 267. Each cow had from 3 – 6 measurements collected during the different stages (7 – 305 DIM) of the first lactation. Information about the cows, herds and pedigree was obtained from the Estonian Animal Recording Centre (EARC), the Animal Breeders’ Association of Estonia and a database, COAGEN®, was produced. The test-day milk yield was recorded and individual milk samples were analyzed for fat percentage, protein percentage and urea content using the MilkoScan 4000 and MilcoScan FT6000, and for SCC using the Fossomatic 4000 and Fossomatic 5000 cell counter at the Milk Analysis Laboratory of the EARC, using methods suggested by the International Committee for Animal Recording (2009). Values of SCC were log-transformed to SCS.
The pH and milk coagulation properties were determined at the Laboratory of Milk Quality of the Estonian University of Life Sciences, usually three days after sampling. The proportion of milk samples with a maximum storage age of seven days was very small, less than 1%. The pH level of the milk was determined using a pH meter (Seven Multi; Mettler Toledo GmbH, Greifensee, Switzerland) at a temperature of 20˚C before analyzing the milk coagulation properties. The latter were milk coagulation time in minutes and firmness of curd in volts. Prior to the assessment of the milk coagulation properties, milk samples were heated to the renneting temperature (35˚C). The rennet (Milase MRS 750 IMCU/ml; CSK Food Enrichment B.V., The Netherlands) used in the analyses was diluted 1:100 (v/v) with distilled water and 0.2 ml of the solution was added to 10 ml milk. The milk coagulation properties were determined using the Optigraph (Ysebaert, Frepillon, France), which was developed by YDD (Ysebaert Dairy Division) in partnership with the INRA (LGMPA, lab. G. CORRIEU) to define coagulation characteristics in the laboratory, specifically to answer the needs of cheese makers (Ysebaert Dairy Division, 2009).
Measurements made with the Optigraph are not based on a rheological method but on an optical signal in the near-infrared spectrum. During a coagulation test, the light transmitted through the milk gradually weakens because of changes in the micellar structure of casein. The Optigraph then calculates the coagulation parameters (coagulation time, curd firmness, speed of aggregation) by means of particular feature points extracted from the optical information acquired in real time (Optigraph User’s Manual).
Blood samples were collected as part of a development project for the Bio-Competence Centre of Healthy Dairy Products in Estonia during the period of June 2005 to December 2007. Blood samples (n=2,959) were stored in tubes containing K3EDTA. DNA was extracted from whole blood according to the method described by (Miller et al., 1998) or by using a commercial Puregene Gentra Blood kit (Minneapolis, USA). The quantity of template DNA was approximately 40 to 100 ng for Allele-specific oligonucleotide (ASO) PCR and PCR-RFLP, respectively. Polymorphisms of five milk protein genes were analyzed, four from the casein cluster
Preliminary analyses for testing the significance of fixed effects and single genotype effects were carried out on the SAS System® (SAS, Cary, NC, USA) using the MIXED procedure. Aggregate β-κ-CN genotypes were formed for further analysis. The genotypes with relative frequencies of less than 1% were grouped together into rare β-κ-CN genotype (A1A1-BB, A1A1-BE, A1A1-EE, A1B-BB, A1B-BE, A2A2-AE, A2A2-BE, A2A3-AA, A2B-AA, A2B-BB, BB-AB, BB-BB). Further statistical analysis was carried out using ASReml (VSN International Ltd., Hemel Hempstead, UK), using the following univariate repeatability animal model:
where
Sample age was included as a covariate in the model only for milk coagulation traits. Sampling year-season and calving year-season were grouped into 3-month classes, 14 classes from April 2005 to June 2008 and 11 classes from December 2004 to August 2007, respectively. Three generations of ancestors with a total number of 17,185 animals in the relationship matrix were included in the analysis.
The class with the largest number of observations, genotype A2A2 -AA was used as the class of comparison. It is also homozygous for both loci. Accordingly, the standard errors of the genotype effects are standard errors of the differences between each genotype and the most frequent A2A2 -AA
Allelic variants of casein and
Protein | Allele | n | Frequency | Protein | Allele | n | Frequency |
κ-CN | A | 4,356 | 0.737 | αs1-CN | B | 5,446 | 0.983 |
B | 1,163 | 0.197 | C | 92 | 0.017 | ||
E | 391 | 0.066 | αs2-CN | A | 5,329 | 0.962 | |
β-CN | A1 | 1,888 | 0.320 | B | 164 | 0.030 | |
A2 | 3,818 | 0.647 | D | 45 | 0.008 | ||
A3 | 5 | 0.001 | β-LG | A | 2,947 | 0.498 | |
B | 191 | 0.032 | B | 2,967 | 0.502 |
Allele frequencies of κ-CN, β-CN, αs1-CN, αs2-CN and β-LG in cows of the Estonian Holstein breed
This finding, of a high frequency of the A2 allele, confirms the advantage of the Estonian Holstein breed that their milk naturally might lower health risks associated with the occurence of the β-CN A1 allele. The other advantage of the β-CN A2 allele is its positive association with protein yield (Olenski et al., 2010). The positive effect of the rare A2A2-BB
Protein | Genotype | n | Frequency | Protein | Genotype | n | Frequency |
κ-CN | AA | 1,606 | 0.544 | αs1-CN | BB | 2,877 | 0.967 |
AB | 850 | 0.288 | BC | 92 | 0.033 | ||
AE | 294 | 0.100 | αs2-CN | AA | 2,764 | 0.926 | |
BB | 116 | 0.039 | AB | 159 | 0.057 | ||
BE | 81 | 0.027 | AD | 42 | 0.015 | ||
EE | 8 | 0.003 | BB | 1 | 0.001 | ||
β-CN | A1A1 | 270 | 0.092 | BD | 3 | 0.001 | |
A1A2 | 1,282 | 0.434 | β-LG | AA | 631 | 0.214 | |
A1B | 66 | 0.022 | AB | 1,685 | 0.570 | ||
A2A2 | 1,212 | 0.411 | BB | 641 | 0.216 | ||
A2A3 | 5 | 0.002 | |||||
A2B | 107 | 0.036 | |||||
BB | 9 | 0.003 |
Genotype frequencies of κ-CN, β-CN, αs1-CN, αs2-CN and β-LG in cows of the Estonian Holstein breed
Expected frequencies of the β-κ-CN genotypes were calculated by multiplying the expected frequencies of the β-CN and κ-CN genotypes. Some alleles at one locus were associated only with certain alleles at the other locus, causing distinct differences between observed and expected frequencies of certain β-κ-CN genotypes (Table 3).
κ-CN | β-CN genotype | ||||||
genotype | A1A1 | A1A2 | A1B | A2A2 | A2A3 | A2B | BB |
AA | 4.0 (117) | 23.1 (683) | 0.0 | 27.1 (801) | 0.2 (5) | 0.1 (2) | 0.0 |
5.0 | 23.6 | 1.2 | 22.4 | 0.1 | 2.0 | 0.2 | |
AB | 1.6 (48) | 10.2 (301) | 1.5 (45) | 12.5 (369) | 2.8 (82) | 0.1 (3) | |
2.6 | 12.5 | 0.6 | 11.8 | 1.0 | 0.1 | ||
AE | 2.5 (73) | 7.2 (214) | 0.0 | 0.2 (7) | 0.0 | 0.0 | |
0.9 | 4.3 | 0.2 | 4.1 | 0.4 | 0.0 | ||
BB | 0.1 (3) | 1.2 (34) | 0.4 (12) | 1.2 (36) | 0.8 (24) | 0.2 (6) | |
0.4 | 1.7 | 0.1 | 1.6 | 0.1 | 0.0 | ||
BE | 0.7 (20) | 1.7 (51) | 0.3 (9) | 0.0 (1) | 0.0 | ||
0.2 | 1.2 | 0.1 | 1.1 | 0.1 | |||
EE | 0.3 (8) | 0.0 | 0.0 | ||||
0.0 | 0.1 | 0.1 |
Observed and expected frequencies (upper and lower line respectively, each given as percentage) of the aggregate β-κ-CN genotypes (numbers of cows in the brackets) in 2,954 Estonian Holstein cows
Some genotypes were observed two to fourfold more frequently than expected (A1A1-BE, A1B-BE, A1B-BB etc) and A2B-BB at eightfold more frequently than expected. All cows carrying the κ-CN EE genotype had association only with the β-CN A1A1 genotype as has also been reported for Finnish Ayrshire cows (Ikonen et al., 1999a). Some genotypes were less frequent than expected (A1A1-BB a quarter and A2A2-AE one-twentieth of the expected frequency). Linkage disequilibrium in the casein
The associations of β-CN and κ-CN genotypes with milk coagulation (RCT, A30), quality traits (SCS, fat and protein contents), and milk yield was investigated (Table 4).
Milk coagulation traits (RCT and A30) were affected by aggregate β-κ-CN genotypes (p<0.001, Table 4). The most favourable β-κ-CN genotypes for RCT included the B allele at both loci, as has also been reported elsewhere (Comin et al., 2008) for Italian Holstein cows. Favourable aggregate genotypes for RCT were A1B-AB and A2B-AB. The best aggregate genotypes for A30 had two B alleles κ-CN, A1A2-BB, and the second best had the genotype A2A2-BB. κ-CN
RCT* (min) | A30 (V) | MILK (kg) | PROTEIN ( %) | FAT (%) | SCS*** | ||||||||
Genotype | N | Est. | SE | Est. | SE | Est. | SE | Est. | SE | Est. | SE | Est. | SE |
β-κ-CN | p<0.001 | p<0.001 | p=0.015 | p<0.001 | p=0.007 | p=0.127 | |||||||
A1A1 -AA | 110 | -0.037 | 0.014 | -0.506 | 0.255 | -1.237 | 0.424 | 0.008 | 0.019 | 0.041 | 0.047 | 0.141 | 0.139 |
A1A1 -AB | 42 | -0.039 | 0.021 | 2.164 | 0.388 | -0.703 | 0.645 | 0.073 | 0.029 | 0.008 | 0.071 | -0.095 | 0.213 |
A1A1 -AE | 70 | -0.033 | 0.016 | -0.894 | 0.305 | -1.132 | 0.508 | 0.053 | 0.023 | 0.035 | 0.056 | 0.311 | 0.168 |
A1A2 -AA | 633 | -0.019 | 0.007 | -0.237 | 0.130 | -0.639 | 0.217 | 0.019 | 0.010 | 0.046 | 0.024 | -0.049 | 0.072 |
A1A2 -AB | 271 | -0.056 | 0.009 | 2.401 | 0.173 | -0.708 | 0.288 | 0.054 | 0.013 | 0.064 | 0.032 | 0.072 | 0.095 |
A1A2 -AE | 207 | -0.009 | 0.010 | -0.683 | 0.190 | 0.028 | 0.317 | -0.004 | 0.014 | -0.035 | 0.035 | 0.051 | 0.105 |
A1A2 - BB | 34 | -0.073 | 0.023 | 4.357 | 0.422 | -2.013 | 0.705 | 0.130 | 0.031 | 0.246 | 0.077 | -0.560 | 0.235 |
A1A2 - BE | 49 | -0.052 | 0.019 | 2.214 | 0.355 | -0.963 | 0.595 | 0.039 | 0.026 | -0.008 | 0.065 | 0.084 | 0.197 |
A1B -AB | 42 | -0.137 | 0.021 | 2.390 | 0.390 | 0.057 | 0.648 | 0.030 | 0.029 | 0.009 | 0.071 | -0.081 | 0.214 |
A2A2 -AA | 768 | 0 | 0 | 0 | 0 | 0 | 0 | ||||||
A2A2 -AB | 337 | -0.017 | 0.009 | 2.157 | 0.161 | -0.383 | 0.268 | 0.057 | 0.012 | 0.036 | 0.029 | -0.144 | 0.088 |
A2A2 - BB | 34 | -0.072 | 0.023 | 3.865 | 0.418 | -0.460 | 0.699 | 0.076 | 0.031 | -0.042 | 0.077 | -0.237 | 0.233 |
A2B -AB | 75 | -0.092 | 0.016 | 1.966 | 0.295 | -0.443 | 0.493 | 0.006 | 0.022 | -0.121 | 0.054 | -0.056 | 0.163 |
Rare** | 93 | -0.084 | 0.014 | 2.018 | 0.267 | -0.453 | 0.445 | 0.029 | 0.020 | -0.051 | 0.049 | -0.168 | 0.148 |
β-LG | p<0.001 | p<0.001 | p=0.462 | p=0.648 | p=0.356 | p=0.571 | |||||||
AA | 589 | -0.015 | 0.006 | -0.426 | 0.127 | 0.181 | 0.194 | 0.000 | 0.009 | -0.016 | 0.021 | -0.047 | 0.065 |
AB | 1,569 | 0 | 0 | 0 | 0 | 0 | 0 | ||||||
BB | 609 | 0.033 | 0.006 | 0.367 | 0.131 | 0.200 | 0.198 | -0.008 | 0.009 | 0.023 | 0.022 | 0.037 | 0.065 |
Statistical significance of milk protein genotypes (p), the number of Estonian Holstein cows (n) per β-κ-CN aggregate genotype and β-LG genotype, estimated genotype effects (Est.) with their standard errors (SE) on milk coagulation time (RCT), curd firmness (A30) and milk production and composition
As for the impact of
Milk yield and protein and fat contents were affected by aggregate β-κ-CN genotype (p<0.05, Table 4). β-κ-CN genotype A1B-AB is favourable for milk yield. Similarly to A30, the most favourable β-κ-CN genotypes for milk protein content were homozygous for the B allele for κ-CN, A1A2-BB and A2A2-BB. The most favourable aggregate genotype for fat content was also A1A2-BB and unfavourable genotype for fat percentage, containing E allele in κ-CN locus, A1A2-AE, but also genotype A2B-AB.
The most favourable for protein content was BB for κ-CN and A1A2 for β-CN (the second best was A2A2, where the A2A2 genotype of β-CN had a slight advantage over the A1A1 genotype). These results were in agreement with those previously reported (Heck, 2009), that the κ-CN genotype was associated with protein content (B>A). Milk with the aggregate genotype A1A2-BB had the best firmness of curd and also the best protein and fat contents. This is in agreement with another investigation (Vallas et al., 2010), where curd firmness had the highest genetic correlation with milk protein percentage (0.48), suggesting that a high protein percentage results in a favourable curd firmness. It has been reported (Cassandro et al., 2008) that there is a correlation coefficient of 0.44 between curd firmness and protein percentage, which is in agreement with the results found in this experiment. The genetic correlations of −0.24 and −0.07 reported (Ikonen et al., 1999a, 2004) for the same traits, however, are different. These inconsistencies indicate that different methodologies used for the investigations may influence the results (Pretto et al., 2011). Curd firmness showed a weak positive genetic correlation (Vallas et al., 2010) with milk fat percentage (0.25) and a weak negative genetic correlation with milk yield (−0.29). Therefore, selection for improved curd firmness may be associated with a somewhat higher protein and fat percentage and slightly reduced milk yield. Genetic correlations for curd firmness with milk yield and fat percentage were negligible in previous studies (Cassandro et al., 2008; Ikonen et al., 1999a). As for the impact of the CSN2 A1 and A2 alleles on milk production, the A2 allele seems to have slight advantage over A1 in the aggregate β-κ-CN genotype. Genotypes of β-LG were associated with both milk coagulation traits (p<0.001), but had no a significant effect on either milk yield (p=0.462), protein percentage (p=0.648), nor fat percentage (p=0.356) and SCS (p=0.571).
The β-κ-CN locus had a strong effect on protein and fat content and milk coagulation properties. Milk with the β-κ-CN aggregate genotype A1A2-BB had the best firmness of curd and also the best protein and fat contents. The aggregate genotype A2A2-BB, haplotype
The research leading to these results is co-financed by the European Community`s Regional Development Fund in the framework of the Competence Centre Programme of the Enterprise Estonia under project No EU22868; EU27789; EU28662; EU30002 of Bio-Competence Centre Of Healthy Dairy Products (Tervisliku Piima Biotehnoloogiate Arenduskeskus OÜ) and by the Targeted Finance Project 1080045s07.
The world’s human population increases by approximately 240,000 people every day: it is expected to reach 8 billion by 2025 and approximately 9.6 billion by 2050. Cultivated land is at a near-maximum, yet estimates predict that food production must be increased by 70% for worldwide peace to persist circa 2050 [1]. Thus, producing sufficient food to meet the ever-growing demand for this rising population is an exceptional challenge to humanity. To succeed at this vital objective, we must build more efficient—yet sustainable—food production devices, farms, and infrastructures. To accomplish that objective, the precision farming concept—a set of methods and techniques to accurately manage variations in the field to increase crop productivity, business profitability, and ecosystem sustainability—has provided some remarkable solutions.
Figure 1 summarizes the cycle of precision agriculture and distinguishes the activities based on analysis and planning (right) from those that rely on providing motion (left). The solutions for activities illustrated in Figure 1 right are being based on information and communication technologies (ICT), whereas the activities on the left rely on tractors, essential devices in current agriculture, that are being automated and robotized and will be also critical in future agriculture (smart farms).
UGVs in the cycle of precision agriculture.
The activities indicated in Figure 1 left can be applied autonomously in an isolated manner, i.e., a fertilization-spreading task, can be performed autonomously if the appropriate implement tank has been filled with fertilizer and attached to a fueled autonomous tractor (UGV); the same concept is applicable to planting and spraying. In addition, harvesting systems must offload the yield every time their collectors are full. However, tasks such as refilling, refueling/recharging, implement attachment, and crop offloading are currently primarily performed manually. The question that arises is: would it be possible to automate all these activities? And if so, would it be possible to combine these activities with other already automated farm management activities to configure a fully automated system resembling the paradigm of the fully automated factory? Then, the combination becomes a fully automated farm in which humans are relegated to mere supervisors. Furthermore, exploiting this parallelism, can we push new developments for farms to mimic the smart factory model? This is the smart farm concept that represents a step forward from the automated farm into a fully connected and flexible system capable of (i) optimizing system performances across a wider network, (ii) learning from new conditions in real- or quasi-real time, (iii) adapting the system to new conditions, and (iv) executing complete production processes in an autonomous way [2]. A smart farm should rely on autonomous decision-making to (i) ensure asset efficiency, (ii) obtain better product quality, (iii) reduce costs, (iv) improve product safety and environmental sustainability, (v) reduce delivery time to consumers, and (vi) increase market share and profitability and stabilize the labor force.
Achieving the smart farm is a long-term mission that will demand design modifications and further improvements on systems and components of very dissimilar natures that are currently being used in agriculture. Some of these systems are outdoor autonomous vehicles or (more accurately) UGVs, which are essential in future agriculture for moving sensors and implementing to cover crop fields accurately and guarantee accurate perception and actuation (soil preparation, crop treatments, harvest, etc.). Thus, this chapter is devoted to bringing forward the features that UGVs should offer to achieve the smart farm concept. Solutions are focused on incorporating the new paradigms defined for smart factories while providing full mobility of the UGVs. These two activities will enable the definition of UGV requirements for smart farm applications.
To this end, the next section addresses the needs of UGVs in smart farms. Then, two main approaches to configure solutions for UGVs in agricultural tasks are described: the automation of conventional vehicles and specifically designed mobile platforms. Their advantages and shortcomings regarding their working features are highlighted. This material enables the definition of other operating characteristics of UGVs to meet the smart farm requirements. Finally, the last section presents some conclusions.
Ground mobile robots, equipped with advanced technologies for positioning and orientation, navigation, planning, and sensing, have already demonstrated their advantages in outdoor applications in industries such as mining [3], farming, and forestry [4, 5]. The commercial availability of GNSS has provided easy ways to configure autonomous vehicles or navigation systems to assist drivers in outdoor environments, especially in agriculture, where many highly accurate vehicle steering systems have become available [6, 7]. These systems aid operators in the precise guidance of tractors using LIDAR (light/laser detection and ranging) or GNSS technology but do not endow a vehicle or tool with any level of autonomy. Nevertheless, other critical technologies must also be incorporated to configure UGVs, such as the safety systems responsible for detecting obstacles in the robots’ path and safeguarding humans and animals in the robots’ surroundings as well as preventing collisions with obstacles or other robots. Finally, robot communications with operators and external servers (cloud technologies) through wireless communications that include the use of cyber-physical systems (CPSs) [8] and Internet of things (IoT) [9] techniques will be essential to incorporate decision-making systems based on big data analysis. Such integration will enable the expansion of decision processes into fields such as machine learning and artificial intelligence. Smart factories are based on the strongly intertwined concepts of CPS, IoT, big data, and cloud computing, and UGVs for smart farms should be based on the same principles to minimize the traditional delays in applying the same technologies to industry and agriculture.
The technology required to deploy more robotic systems into agriculture is available today, as are the clear economic and environmental benefits of doing so. For example, the global market for mobile robots, in which agricultural robots are a part, is expected to increase at a compound annual growth rate of over 15% from 2017 to 2025, according to recent forecast reports [10]. Nevertheless, manufacturers of agricultural machinery seem to be reluctant to commercialize fully robotic systems, although they have not missed the marketing potential of showing concepts [11, 12]. In any event, according to the Standing Committee on Agricultural Research [13], further efforts should be made by both researchers and private companies to invent new solutions.
Most of the robotics and automation systems that will be used in precision agriculture—including systems for fertilizing, planting, spraying, scouting, and harvesting (Figure 1)—will require the coordination of detection devices, agricultural implements, farm managing systems, and UGVs. Thus, several research groups and companies have been working on such systems. Specifically, two trends can be identified in the development of UGVs: the automation of conventional agricultural vehicles (tractors) and the development of specifically designed mobile platforms. The following sections discuss these two types of vehicles.
The tractor has been the central vehicle for executing most of the work required in a crop field. Equipped with the proper accessories, this machine can till, plant, fertilize, spray, haul, mow, and even harvest. Their adaptability to dissimilar tasks makes tractors a prime target for automation, which would enable productivity increases, improve safety, and reduce operational costs. Figure 2 shows an example of the technologies and equipment for automating agricultural tractors.
An example of agricultural tractor automation‑distribution of sensorial and actuation systems for transforming an agricultural tractor into a UGV (Gonzalez-de-Santos et al., 2017).
Numerous worldwide approaches to automating diverse types of tractors have been researched and developed since 1995 when the first GNSS was made available to the international civilian community of users, which opened the door for GPS-guided agricultural vehicles (auto-steering) and controlled-traffic farming.
The first evaluations of GPS systems for vehicle guidance in agriculture were also published in 1995 [14] demonstrating its potential and encouraging many research groups around the world to automate diverse types of tractors. The earliest attempts were made at Stanford University in 1996, where an automatic control system for an agricultural tractor was developed and tested on a large farm [15]. The system used a location system with four GPS antennas. Around the same time, researchers at the University of Illinois, USA, developed a guidance system for an autonomous tractor based on sensor fusion that included machine vision, real-time kinematics GPS (RTK-GPS), and a geometric direction sensor (GDS). The fusion integration methodology was based on an extended Kalman filter (EKF) and a two-dimensional probability-density-function statistical method. This system achieved a lateral average error of approximately 0.084 m at approximately 2.3 m s−1 [16].
A few years later, researchers at Carnegie Mellon University, USA, developed some projects that made significant contributions. The Demeter project was conceived as a next-generation self-propelled hay harvester for agricultural operations, and it became the most representative example of such activity [17]. The positional data was fused from a differential GPS, a wheel encoder (dead reckoning), and gyroscopic system sensors. The project resulted in a system that allowed an expert harvesting operator to harvest a field once, thus programming the field. Subsequently, an operator with lesser skill could “playback” the programmed field at a later date. The semi-autonomous agricultural spraying project, developed by the same research group, was devoted to making pesticide spraying significantly cheaper, safer, and more environmentally friendly [18]. This system enabled a remote operator to oversee the nighttime operation of up to four spraying vehicles. Another example is research conducted at the University of Florida, USA, [19], in which two individual autonomous guidance systems for use in a citrus grove were developed and tested along curved paths at a speed of approximately 3.1 m s−1. One system, based on machine vision, achieved an average guidance error of approximately 0.028 m. The other system, based on LIDAR guidance, achieved an average error of approximately 0.025 m.
Similar activities started in Europe in the 2000s. One example is the work performed at LASMEA-CEMAGREF, France, in 2001, which evaluated the possibilities of achieving recording-path tracking using a carrier phase differential GPS (CP-DGPS), as the only sensor. The vehicle heading was derived according to a Kalman state reconstructor and a nonlinear velocity independent control law was designed that relied on chained systems properties [20].
A relevant example of integrating UGVs with automated tools is the work conducted at the University of Aarhus and the University of Copenhagen, Denmark [21]. The system comprised an autonomous ground vehicle and a side shifting arrangement affixed to a weeding implement. Both the vehicle and the implement were equipped with RTK-GPS; thus, the two subsystems provided their own positions, allowing the vehicle to follow predefined GPS paths and enabling the implement to act on each individual plant, whose positions were automatically obtained during seeding.
Lately, some similar automations of agricultural tractors have been conducted using more modern equipment [22, 23], and some tractor manufacturers have already presented noncommercial autonomous tractors [11, 12]. This tendency to automate existing tractors has been applied to other types of lightweight vehicles for specific tasks in orchards such as tree pruning and training, blossom and fruit thinning, fruit harvesting, mowing, spraying, and sensing [24]. Table 1 summarizes the UGVs based on commercial vehicles for agricultural tasks.
Institution | Year | Description |
---|---|---|
Stanford University (USA) [15] | 1996 | Automatic large-farm tractor using 4 GPS antennas |
University of Illinois (USA) [16] | 1998 | A guidance system using a sensor based on machine vision, an RTK-GPS, and a GDS |
Carnegie Mellon University (USA)—Demeter project [17] | 1999 | A self-propelled hay harvester for agricultural operations |
Carnegie Mellon University (USA)—Autonomous Agricultural Spraying project [18] | 2002 | A ground-based vehicles for pesticide spraying |
LASMEA-CEMAGREF (France) [20] | 2001 | This study investigated the possibility of achieving vehicle guiding using a CP-DGPS as the only sensor |
University of Florida (USA) [19] | 2006 | An autonomous guidance system for citrus groves based on machine vision and LADAR |
University of Aarhus and the University of Copenhagen (Denmark) [21] | 2008 | An automatic intra-row weed control system connected to an unmanned tractor |
RHEA consortium (EU) [22] | 2014 | A fleet (3 units) of tractors that cooperated and collaborated in physical/chemical weed control and pesticide applications for trees |
Carnegie Mellon University (USA) [24] | 2015 | Self-driving orchard vehicles for orchard tasks |
University of Leuven (Belgium) [23] | 2015 | Tractor guidance using model predictive control for yaw dynamics |
UGVs based on commercial vehicles.
Nevertheless, UGVs suitable for agriculture remain far from commercialization, although many intermediate results have been incorporated into agricultural equipment—from harvesting to precise herbicide application. Essentially, these systems are installed on tractors owned by farmers and generally consist of a computer (the controller), a device for steering control, a localization system (mostly based on RTK-GPS), and a safety system (mostly based on LIDAR). Many of these systems are compatible only with advanced tractors that feature ISOBUS control technology [25], through which controllers connected to the ISOBUS can access other subsystems of the tractor (throttle, brakes, auxiliary valves, power takeoff, linkage, lights, etc.). Examples of these commercial systems are AutoDrive [26] and X-PERT [27].
An important shortcoming of these solutions is their lack of intelligence in solving problems, especially when obstacles are detected because they are not equipped with technology suitable for characterizing and identifying the obstacle type. This information is essential when defining any behavior other than simply stopping and waiting for the situation to be resolved. Another limitation of this approach is that the conventional configuration of a standard tractor driven by an operator is designed to maximize the productivity per hour; thus, the general architecture of the system (tractor plus equipment) is only roughly optimized.
The second approach to the configuration of mobile robots for agriculture is the development of autonomous ground vehicles with specific morphologies, where researchers develop ground mobile platforms inspired more by robotic principles than by tractor technologies. These platforms can be classified based on their locomotion system. Ground robots can be based on wheels, tracks, or legs. Although legged robots have high ground adaptability (that enables the vehicles to work on irregular and sloped terrain) and intrinsic omnidirectionality (which minimizes the headlands and, thus, maximizes croplands) and offer soil protection (discrete points in contact with the ground that minimize ground damage and ground compaction, an important issue in agriculture), they are uncommon in agriculture; however, legged robots provide extraordinary features when combined with wheels that can configure a disruptive locomotion system for smart farms. Such a structure (which consists of legs with wheels as feet) is known as a wheel-legged robot. The following sections present the characteristics, advantages, and disadvantages of these specifically designed types of robots.
The structure of a wheeled mobile platform depends on the following features:
Passive wheel: The wheel rotates freely around its shaft and does not provide power.
Active wheel: An actuator rotates the wheel to provide power.
Coordinated steering scheme: Two fixed active wheels at the rear of the platform coupled with two passive orienting wheels at the front of the platform are the most common wheel arrangement for vehicles. To maintain all wheels in a pure rolling condition during a turn, the wheels need to follow curved paths with different radii originating from a common center [29]. A special steering mechanism, the Ackermann steering system, which consists of a 4-bar trapezoidal mechanism (Figure 3a), can mechanically manage the angles of the two steering wheels. This system is used in all the vehicles presented in Table 2. It features medium mechanical complexity and medium control complexity. One advantage of this system is that a single actuator can steer both wheels. However, independent steering requires at least three actuators for steering and power (Figure 3b).
Skid steering scheme: Perhaps the simplest structure for a mobile robot consists of four fixed, active wheels, one on each corner of the mobile platform. Skid steering is accomplished by producing a differential thrust between the left and right sides of the vehicle, causing a heading change (Figure 3c). The two wheels on one side can be powered independently or by a single actuator. Thus, the motion of the wheels in the same direction produces backward/forward platform motion; and the motion of the wheels on one side in the opposite direction to the motion of wheels on the other side produces platform rotation.
Independent steering scheme: An independent steering scheme controls each wheel, moving it to the desired orientation angle and rotation speed (Figure 3d). This steering scheme makes wheel coordination and wheel position accuracy more complex but provides some advantages in maneuverability. In addition, this scheme provides crab steering (sideways motion at any angle α; 0 ≤ α ≤ 2π) by aligning all wheels at an angle α with respect to the longitudinal axis of the mobile platform. Finally, the coordination of driving and steering results in more efficient maneuverability and reduces internal power losses caused by actuator fighting. The independent steering scheme requires eight actuators for a four-wheel vehicle.
Steering driving systems: (a) Ackermann steering system; (b) independent steering; (c) skid steering system and (d) independent steering and traction system.
Steering scheme | Characteristics |
---|---|
Coordinated | Advantages:
|
Skid | Advantages:
|
Independent | Advantages:
|
Characteristics of wheeled structures.
Table 2 summarizes the advantages and drawbacks of these schemes. Note that the number of actuators increases the total mass of a robot as well as its mechanical and control complexity (more motors, more drivers, more elaborate coordinating algorithms, etc.).
Some examples of wheeled mobile platforms for agriculture are the conventional tractor using the Ackermann steering system (Figure 2) with two front passive and steerable wheels and two rear fixed and active wheels.
Skid steering platforms can be found in many versions. For example,
Four fixed wheels placed in pairs on both sides of the robot
Two fixed tracks, each one placed longitudinally at each side of the robot,
Two fixed wheels placed at the front of the robot and two castor wheels placed at the rear (Figure 4c), etc.
Pictures of several specifically-designed agricultural platforms. (a) Robot for weed detection, courtesy of T. Bak, Department of Agricultural Engineering, Danish Institute of Agricultural Sciences; (b) ladybird, courtesy of J. P. Underwood, Australian Centre for Field Robotics at the University of Sydney [
Regarding the independent steering scheme, the robot developed by Bak and Jakobsen [30] is one of the first representative examples (Figure 4a). This platform was designed specifically for agricultural tasks in wide-row crops and featured good ground clearance (approximately 0.5 m) and 1-m wheel separation. The platform is based on four-identical wheel modules. Each one includes a brushless electric motor that provides direct-drive power, and steering is achieved by a separate motor.
An example of a mobile platform under development that focuses on performing precision agricultural tasks is AgBot II (Figure 4c). This is a platform that follows the skid steering scheme with two front fixed wheels (working in skid or differential mode) and two rear caster wheels. It is intended to work autonomously on both large-scale and horticultural crops, applying fertilizer, detecting and classifying weeds, and killing weeds either mechanically or chemically [31, 32]. Another robot is Robot for Intelligent Perception and Precision Application (RIPPA), which is a light, rugged, and easy-to-operate prototype for the vegetable growing industry. It is used for autonomous high-speed, spot spraying of weeds using a directed micro-dose of liquid when equipped with a variable injection intelligent precision applicator [33]. Another example is Ladybird (Figure 4b), an omnidirectional robot powered with batteries and solar panels that follows the independent steering scheme. The robot includes many sensors (i.e., hyperspectral cameras, thermal and infrared detecting systems, panoramic and stereovision cameras, LIDAR, and GPS) that enable assessing crop properties [34]. One more prototype, very close to commercialization, is Kongskilde Vibro Crop Robotti, which is a self-contained track-based platform that uses the skid steering scheme. It can be equipped with implements for precision seeding and mechanical row crop cleaning units. This robot can work for 2–4 hours at a 2–5 km h−1 rate and is supplied by captured electric energy [35].
These robots are targeted toward fertilizing, seeding, weed control, and gathering information, and they have similar characteristics in terms of weight, load capacity, operational speed, and morphology. Tools, instrumentation equipment, and agricultural implements are connected under the robot, and tasks are performed in the area just below the robot, which optimizes implement weight distribution. These robots have limitations for use on farmland with substantial (medium to high) slopes or gully erosion. Nevertheless, some mobile platforms are already commercially available. Two examples of these vehicles are the fruit robots Cäsar [36] and Greenbot [37].
Cäsar is a remote-controlled special-purpose vehicle that can perform temporarily autonomous operations in orchards and vineyards such as pest management, soil management, fertilization, harvesting, and transport. Similarly, Greenbot is a self-driving machine specially developed for professionals in the agricultural and horticultural sectors who perform regular, repetitious tasks. This vehicle can be used not only for fruit farming, horticulture, and arable farming but also in the urban sector and even at waterfronts or on roadsides.
Despite their current features, the existing robots lack flexibility and terrain adaptability to cope with diverse scenarios, and their safety features are limited. For example:
They focus only on orchard and vineyard activities.
They have ground clearance limitations.
They are unsuitable for rough terrain or slopes.
They must be manually guided to the working area rather than freely and autonomously moving to different working areas around the farm.
They possess no advanced detection systems for weed or soil identification, which limits their use to previously planned tasks related to selective treatment.
They lack dynamic safety systems capable of recognizing or interpreting safety issues; thus, they are incapable of rescheduling or solving problems by themselves.
In addition, existing UGVs for agriculture lack communication mechanisms for providing services through cloud technologies, CPS, and IoT techniques, crucial instruments to integrate decision-making systems based on big data analysis, as is being done in the smart factory concept.
Table 3 summarizes the diverse robotic platforms, and Figure 4 depicts some of these platforms.
Vehicle | Type* | Year | Description |
---|---|---|---|
AgBot II [32] | P | 2014 | A platform that follows the skid steering scheme with two front fixed wheels (working in skid or differential mode) and two rear caster wheels |
Ladybird [34] | P | 2015 | An omnidirectional robot powered with batteries and solar panels that uses the independent steering scheme |
Greenbot [37] | C | 2015 | A self-driving robot for tasks in agriculture and horticulture |
Cäsar [36] | P | 2016 | A remotely controlled platform for temporary, autonomous use in fruit plantations and vineyards |
RIPPA [33] | P | 2016 | A light, rugged, and easy-to-operate prototype for the vegetable growing industry |
Vibro Crop Robotti [35] | C | 2017 | A self-contained track-based platform that uses the skid steering scheme |
Robots designed specifically for agriculture.
P-prototype; C-commercial.
The structure of a wheel-legged mobile platform depends on (i) the number of legs, (ii) the leg type, and (iii) the leg arrangement. The feet consist of 2-DOF steerable powered wheels as illustrated in Figure 5.
Wheel-legged structures. (a) 4-DOF articulated leg; (b) 3-DOF SCARA leg; (c) 2-DOF SCARA leg; (d) 1-DOF leg.
Structure | Characteristics |
---|---|
A 4-DOF articulated leg with a 2-DOF wheeled foot (Figure 5a) | Advantages:
|
A 3-DOF motion-decoupled leg* with a 2-DOF wheeled foot (Figure 5b) | Advantages:
|
A 2-DOF motion-decoupled leg* with a 2-DOF wheeled foot (Figure 5c) | Advantages:
|
A 1-DOF leg with a 2-DOF wheeled foot (Figure 5d) | Advantages:
|
Wheel-legged structures.
Cylindrical, Selective Compliant Articulated Robot Arm (SCARA) or Cartesian.
Figure 6a illustrates the structure scheme of a wheel-legged robot based on the 3-DOF SCARA leg (See Figure 5b) with full terrain adaptability, ground clearance control, crop adaptability, and capability of walking, and Figure 6b shows the structure of a wheel-legged robot exhibiting full terrain adaptability, ground clearance control, and crop adaptability; however, it cannot walk under static stability.
Model of wheel-legs: (a) full terrain-crop adaptability, (b) full terrain and partial crop adaptability.
Another interesting example is the structure of BoniRob [39], a real wheel-legged platform for multipurpose agriculture applications, which consists of four independently steerable powered wheeled legs with the structure illustrated in Figure 5d (1-DOF legs with a 2-DOF wheeled foot). This robot can adjust the distance between its wheel sets, making it adaptable to many agricultural scenarios. The platform can be equipped with common sensorial systems used in robotic agricultural applications, such as LIDAR, inertial sensors, wheel odometry, and GPS. Moreover, the robotic platform can be retrofitted and upgraded with swappable application modules or tools for crop and weed identification, plant breeding applications, and weed control. This robotic platform is completely powered by electricity, which is more environmentally friendly but reduces its operational working time compared to conventional combustion-engine systems. Nevertheless, this robot configuration requires custom-built implements, which prevent the reuse of existing implements and, thus, jeopardize the introduction of this robot to the agricultural market.
In addition to their needed characteristics for infield operations, the robots fulfilling the demands of a smart farm will require the operating requirements summarized in the following paragraphs and Table 5.
Characteristics | Value |
---|---|
Dimensions | Length: ~3.0 m; width: ~1.50 m; height: ~1.00 m |
Weight | 1200–1700 kg |
Payload | 500–1000 kg |
Comments: These characteristics are estimations based on the current medium-sized vehicles reported in this chapter that are capable of carrying agricultural implements. Robots for carrying sensing systems can be truly small (low payloads), but vehicles for treatments need to carry medium to heavy loads (pesticides, fertilizes, etc.). For example, existing sprayers [45] weigh approximately 600–700 kg including 200–300 L of active ingredient. | |
Speed | 3–25 km h−1 |
Comments: Treatment speed is limited by the treatment process that depends on physical laws. However, robots need to move among working fields minimizing moving time; therefore, they must feature a reasonably high top speed. | |
Position accuracy | ±0.02 m |
Comments: The current DGPS accuracy seems to be sufficient for real applications. However, specific real-time localization systems, RTLS, can be used in small areas where GNSS is unavailable (radio frequency identification tags (RFID), ultra-wide band tags (UWB), etc.). These technologies will be essential in smart farms to ensure positioning precision in GNSS occluded areas. | |
Clearance | 0.35–1 m |
Comments: Weed control is performed at an early crop-growth stage; therefore, the minimum ground clearance of the robot must be approximately 0.35 m. A ground clearance of approximately 1 m will facilitate application of treatments at later crop-growth stages. The ideal approach would be to control the ground clearance to optimize the working height of the implements based on the crop. Existing robots cannot control their ground clearance, but some wheel-legged configurations can meet this specification (Figure 5a,b, and c). | |
Track width | 1.50–2.25 m |
Comments: To preserve crops in narrow-row situations, a tramline control is required; however, in wide-row crops, the tramlines must be located in the inter-row spacing. Taking maize as an example, which is planted at an inter-row spacing of approximately 0.75 m in some areas in Europe, a robot track width of 1.50 to 2.25 m is required to enable 2 or 3 rows to pass under the robot’s body. Controlling robot track width is imperative in a smart farm world. This characteristic is exhibited by wheeled-legged robots, which makes them a good candidate for UGVs in smart farms. | |
Energetic autonomy | ~10 h |
Comments: Robots based on combustion engines (e.g., tractors) can operate autonomously for approximately 10 hours, at minimum. The duration of autonomous operation for electrically driven systems should be similar. Some existing prototypes already meet this expectation [31]. In any case, the increasing improvement in battery technology will enlarge the energetic autonomy of future vehicles and robots. |
Prospective characteristics for UGVs in smart farms.
Although conventional tractors are proven and highly reliable machines, they lack some adaptability features. Tractors have normally fixed distances between wheels, which makes them unsuitable for working on crops with different distances between rows. Using mobile platforms capable of controlling the distance between wheels could alleviate this problem, allowing the machines to adapt to different crops under different situations.
A steering system capable of zero-radius turns would be a proper solution, and this feature can be implemented by different structures as discussed in the previous section. Thus, minimization of headlands and wheel distance control can be achieved using either conventional or new articulated structures. Among the conventional structures, the skid steering scheme based on wheels or tracks is capable of zero-radius turns without additional steering mechanism, which helps in minimizing the headlands. However, separating and controlling the distance between contralateral wheels/tracks requires an active system (which already exists for some tracked vehicles used in the building industry).
Mobile platform structures based on coordinated or independent steering schemes can achieve zero-radius turns, but they still lack intrinsic track width control and require additional mechanisms. Another structure is the wheel-legged mechanism. Legged robots exhibit high terrain adaptability on irregular ground, but wheeled robots have speed advantages on smooth terrain; that is, they complement each other. Therefore, the most complete wheel-legged mechanism (Figure 6a) is a leg with three degrees of freedom [38] with an active wheel as a foot, where the wheel is steered and driven separately. This is a disruptive design not verified yet that will provide extraordinary characteristics to robots for smart farm applications. Thus, the wheels drive and steer, while the legs provide track-width control and terrain adaptation, i.e., they control the robot’s body leveling and ground clearance. This is the most capable system regarding ground clearance and body pose control, but it comes at the cost of higher mechanical complexity. Nevertheless, intermediate solutions can be developed to reduce the number of actuators while maintaining appropriate robot characteristics. Table 4 summarizes different wheel-legged theoretical solutions indicating advantages and shortcomings, and Figure 5 shows some sketches of practical solutions.
Minimize energy consumption by optimizing the robot trajectories during the mission
Drastically reduce the use of herbicides and fertilizers by using intelligent detection systems, tools, and decision-making algorithms
Eliminate the need for a driver and minimize operator risk
Minimize unnecessary crop damage and soil compaction
Wireless communications with the operator and/or a central controller for control commands and data exchanges, including images and real-time video, will be required. Wireless communication among robots will also be required for coordination and collaboration.
Safety for humans and robots can usually be accomplished through a combination of computer vision, LIDAR, and proximity sensors to infer dangerous situations and halt robot motion, whereas safety to crops is achieved through precise steering that guides the robot to follow the crop rows accurately using the crop position acquired at seeding time or real-time crop-detection systems. Following these three stages, a step forward in safety for agricultural robots would be the integration of a two-level safety system relying on the following:
Regardless of the exact approach, standards on safety machinery must be taken into consideration [42] to ensure that systems will meet regulations and will be able to achieve certification.
Based on the existing agricultural vehicles and robot prototypes, robots to be deployed in smart farms should meet also the characteristics presented in Table 5.
The world population is increasing rapidly, causing a demand for more efficient production processes that must be both safe and respect the ecosystem. Industry has already planned to meet production challenges in the coming decades by defining the concept of the smart factory; the agriculture sector should follow a similar path to design the concept of the smart farm: a system capable of optimizing its performance across a wide network, learning from new conditions in real time and adapting the system to them and executing the complete production process in an autonomous manner. Smart factory and smart farm concepts have many commonalities and include some common solutions, but some specific aspects of smart farms should be studied separately. For example, the design of UGVs for outdoor tasks in agriculture (field robots) presents specific characteristics worthy of explicit efforts.
This chapter focused on reviewing the past and present developments of UGVs for agriculture and anticipated some characteristics that these robots should feature for fulfilling the requirements of smart farms. To this end, this chapter presented and criticized two trends in building UGVs for smart farms based on (i) commercial vehicles and (ii) mobile platforms designed on purpose. The former has been useful for evaluating the advantages of UGV in agriculture, but the latter offers additional benefits such as increased maneuverability, better adaptability to crops, and improved adaptability to the terrain. Clearly, independent-steering and skid-steering systems provide the best maneuverability, but depending on their complexity, wheel-legged structures can provide similar maneuverability and improved adaptability to crops and terrain as well as increased stability on sloped terrain. For example, the 4-DOF articulated wheeled leg (Figure 5a) and the 3-DOF SCARA leg (Figure 5b and 6a) exhibit the best features at the cost of being the most complex. Note that although both structures have the same maneuverability features and adaptability to crops and terrain (ground clearance, body leveling, etc.), the 3-DOF SCARA leg involves one fewer motor per leg, which decreases the price and weight and improves the reliability of the robot. However, the 2-DOF SCARA leg also exhibits useful features regarding maneuverability, adaptability to crops, and adaptability to terrain (ground clearance control and body leveling) while using fewer actuators (Figure 5c and 6b). For agricultural tasks carried out on flat terrain, the 1-DOF leg with a 2-DOF wheeled foot provides sufficient maneuverability and adaptability to crops with very few actuators (leg structure as in Figure 5d).
However, these robots also require some additional features to meet the needs of the smart farm concept, such as the following:
Flexibility to work on very dissimilar scenarios and tasks.
Maneuverability to perform zero-radius turns, crab motion, etc.
Resilience to recover itself from malfunctions.
Efficiency in the minimization of pesticide and energy usage.
Intuitive, reliable, comfortable, and safe HMIs attractive to nonrobotic experts to ease the introduction of robotic systems in agriculture.
Wireless communications to communicate commands and data among the robots, the operator, and external servers for enabling CPSs, IoT, and cloud computing techniques to support services through the Internet.
Safety systems to ensure safe operations to humans, crops, and robots.
Environmental impact by reducing chemicals in the ground and pollutants into the air.
Standards: operational robots have to meet the requirements and specifications of the standards in force for agricultural vehicles.
Implement usage: although specific onboard implements for UGV are appearing, the capability of also using conventional implements will help in the acceptation of new technologies by farmers and, hence, the introduction of new-generation robotic systems.
Autonomy: both behavioral autonomy and operation autonomy. Regarding power supplies, automobiles worldwide will likely be electric vehicles powered by batteries within the next few decades; thus, agricultural vehicles should embrace the same solution.
Regardless of these characteristics, UGVs for smart farms have to fulfill the requirements of multi-robot systems, which is a fast-growing trend [22, 40, 46]. Multi-robot systems based on small-/medium-sized robots can accomplish the same work as a large machine, but with better positioning accuracy, greater fault tolerance, and lighter weights, thus reducing soil compaction and improving safety. Moreover, they can support mission coordination and reconfiguration. These capabilities position small/medium multi-robot systems as prime future candidates for outdoor UGVs in agriculture. Additionally, UGVs for smart farms should exhibit some quantitative physical characteristics founded on past developments and current studies that are summarized in Table 5.
Finally, autonomous robots of any type, working in fleets or alone, are essential for the precision application of herbicides and fertilizers. These activities reduce the use of chemicals generating important benefits: (i) a decrease in the cost of chemical usage, which impacts in the system productivity; (ii) an improvement in safety for operators, who are moved far from the vehicles; (iii) better health for the people around the fields, who are not exposed to the effects of chemical; and (iii) improved quality of foods that will reduce the content of toxic products.
The research leading to these results has received funding from (i) RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub (“Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase IV”; S2018/NMT-4331), funded by “Programas de Actividades I+D en la Comunidad de Madrid” and cofunded by Structural Funds of the EU; (ii) the Agencia Estatal Consejo Superior de Investigaciones Científicas (CSIC) under the BMCrop project, Ref. 201750E089; and (iii) the Spanish Ministry of Economy, Industry and Competitiveness under Grant DPI2017-84253-C2-1-R.
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