Scientific publications

Leafiness-LiDAR index and NDVI for identification of temporal patterns in super-intensive almond orchards as response to different management strategies.
Sandonís-Pozo, L., Oger, B., Tisseyre, B., Llorens, J., Escolà, A., Pascual, M., Martínez-Casasnovas, J.A. 2024.
European Journal of Agronomy 159, 127278. DOI: https://doi.org/10.1016/j.eja.2024.127278

A systematic analysis of scan matching techniques for localization in dense orchards. 
Guevara J, Gené-Mola J, Gregorio E, Auat Cheein FA. 2024.
Smart Agricultural Technology 9, 100607. DOI: https://doi.org/10.1016/j.atech.2024.100607 

Design and characterization of a real-time capacitive system to estimate pesticides spray deposition and drift.
Pallejà, T., Tresanchez, M., Llorens, J., Saiz-Vela, A. 2023.
Computers and Electronics in Agriculture 207, 107720. DOI:  https://doi.org/10.1016/j.compag.2023.107720

AmodalAppleSize_RGB-D dataset: RGB-D images of apple trees annotated with modal and amodal segmentation masks for fruit detection, visibility and size estimation
Gené-Mola J, Ferrer-Ferrer M, Jochen H, van Dalfsen P, de Hoog D, Sanz-Cortiella R, Rosell- Polo JR, Morros JR, Vilaplana V, Ruiz-Hidalgo J, Gregorio E. 2023.
Data in Brief, 52, 110000. DOI: https://doi.org/10.1016/j.dib.2023.110000

Relationship between yield and tree growth in almond as influenced by nitrogen nutrition
Sandonís-Pozo L, Martínez-Casasnovas JA, Llorens J, Escolà A, Arnó J, Pascual M. 2023.
Scientia Horticulturae, 321, 112353. DOI: https://doi.org/10.1016/j.scienta.2023.112353

Assessing automatic data processing algorithms for RGB-D cameras to predict fruit size and weight in apples 
Miranda JC, Arnó J, Gené-Mola J, Lordan J, Asín L, Gregorio E. 2023.
Computers and Electronics in Agriculture, 214, 108302. DOI: https://doi.org/10.1016/j.compag.2023.108302 

AKFruitYield: Modular benchmarking and video analysis software for Azure Kinect cameras for fruit size and fruit yield estimation in apple orchards
Miranda JC, Arnó J, Gené-Mola J, Fountas S, Gregorio E. 2023.
SoftwareX, 24, 101548. DOI: https://doi.org/10.1016/j.softx.2023.101548

Drip Irrigation Soil-Adapted Sector Design and Optimal Location of Moisture Sensors: A Case Study in a Vineyard Plot
Arnó J, Uribeetxebarria A, Llorens J, Escolà A, Rosell-Polo JR, Gregorio E, Martínez-Casasnovas JA. 2023.
Agronomy, 13, 2369. DOI: https://doi.org/10.3390/agronomy13092369

Fruit sizing using AI: A review of methods and challenges 
Miranda JC, Gené-Mola J, Zude-Sasse M, Tsoulias N, Escolà A, Arnó J, Rosell-Polo JR, Sanz-Cortiella R, Martínez-Casasnovas JA, Gregorio E. 2023.
Postharvest Biology and Technology 206, 112587. DOI: https://doi.org/10.1016/j.postharvbio.2023.112587 

Looking behind occlusions: A study on amodal segmentation for robust on-tree apple fruit size estimation
Gené-Mola J, Ferrer-Ferrer M, Gregorio E, Blok PM, Hemming J, Morros JR, Rosell-Polo JR, Vilaplana V, Ruiz-Hidalgo J. 2023.
Computers and Electronics in Agriculture 209, 107854. DOI: https://doi.org/10.1016/j.compag.2023.107854

Mobile terrestrial laser scanner vs. UAV photogrammetry to estimate woody crop canopy parameters – Part 1: Methodology and comparison in vineyards
Escolà, A., Peña, J.M., López-Granados, F., Rosell-Polo, J.R., de Castro, A., Gregorio, E., Jiménez-Brenes, F.M., Sanz, R., Sebé, F., Llorens, J., Torres-Sánchez, J. 2023.
Computers and Electronics in Agriculture 212, 108109. DOI: https://doi.org/10.1016/j.compag.2023.108109

Mobile terrestrial laser scanner vs. UAV photogrammetry to estimate woody crop canopy parameters – Part 2: Comparison for different crops and training systems
Torres-Sánchez, J., Escolà, A., de Castro, A., López-Granados, F., Rosell-Polo, J.R., Sebé, F., Jiménez-Brenes, F.M., Sanz, R., Gregorio, E., Peña, J.M. 2023.
Computers and Electronics in Agriculture 212, 108083. DOI: https://doi.org/10.1016/j.compag.2023.108083

Organic mulches as an alternative for under-vine weed management in Mediterranean irrigated vineyards:
Impact on agronomic performance
Cabrera-Pérez C, Llorens J, Escolà A, Royo-Esnal A, Recasens J. 2023.
European Journal of Agronomy 145, 126798. DOI: https://doi.org/10.1016/j.eja.2023.126798

Simultaneous fruit detection and size estimation using multitask deep neural networks
Ferrer-Ferrer, M., Ruiz-Hidalgo, J., Gregorio, E., Vilaplana, V., Morros, J.R., Gené-Mola, J. 2023.
Biosystems Engineering, 233, 63-75. DOI: https://doi.org/10.1016/j.biosystemseng.2023.07.010

AKFruitData: A dual software application for Azure Kinect cameras to acquire and extract informative data in yield tests performed in fruit orchard environments  
Miranda JC, Gené-Mola J, Arnó J, Gregorio E. 2022. 
SoftwareX, 20, 101231. DOI: https://doi.org/10.1016/j.softx.2022.101231

Satellite multispectral indices to estimate canopy parameters and within-field management zones in super-intensive almond orchards  
Sandonís-Pozo L, Llorens J, Escolà A, Arno J, Pascual M, Martinez-Casasnovas JA. 2022. 
Precision Agriculture 23, 2040-2062. DOI: https://doi.org/10.1007/s11119-022-09956-6

Delineation of Management Zones in Hedgerow Almond Orchards Based on Vegetation Indices from UAV Images Validated by LiDAR-Derived Canopy Parameters 
Martinez-Casasnovas JA, Sandonis-Pozo L, Escolà A, Arnó J, Llorens J. 2022. 
Agronomy, 12(1), 102. DOI: https://doi.org/10.3390/agronomy12010102

Evaluation of a boxwood topiary trimming robot 
Marrewijk M, Vroegindeweij B, Gené-Mola J, Mencarelli A, Hemming J, Mayer N, Maximilian W, Kootstra G. 2022. 
Biosystems Engineering, 214 (2022), 11-27. DOI: https://doi.org/10.1016/j.biosystemseng.2021.12.001

Bases for pesticide dose expression and adjustment in 3D crops and comparison of decision support systems
Planas S, Román C,Sanz R, Rosell-Polo JR. 2022
Science of the Total Environment, 806 (2022), 150357. DOI: https://doi.org/10.1016/j.scitotenv.2021.150357

Pesticide dose adjustment in fruit and grapevine orchards by DOSA3D : Fundamentals of the system and on-farm validation
Román C, Peris M, Esteve J, Tejerina M, Cambray J, Vilardell P, Planas S. 2022. 
Science of the Total Environment, 808 (2022), 152158. DOI: https://doi.org/10.1016/j.scitotenv.2021.152158

Remote Sensing Imaging as a Tool to Support Mulberry Cultivation for Silk Production
Giora D., Assirelli A., Cappellozza S., Sartori L., Saviane A., Marinello F., Martínez-Casasnovas J.A. 2022
Remote Sensing 14(21), 5450. DOI: https://doi.org/10.3390/rs14215450

A cheap electronic sensor automated trap for monitoring the flight activity period of moths
Pérez Aparicio, A., Llorens Calveras, J., Rosell Polo, J. R., Martí, J., & Gemeno Marín, C. 2021.
European Journal Of Entomology 118, pp. 315-321. DOI: https://doi.org/10.14411/eje.2021.032

Spatially variable pesticide application in olive groves: Evaluation of potential pesticide-savings through stochastic spatial simulation algorithms.
Rodríguez-Lizana, A., Pereira, M.J., Ribeiro, M.C., Soares, A., Azevedo, L., Miranda-Fuentes, A., Llorens, J. 2021. 
Science of The Total Environment 778, 146111. DOI: https://doi.org/10.1016/j.scitotenv.2021.146111

A photogrammetry-based methodology to obtain accurate digital ground-truth of leafless fruit trees
Lavaquiol B, Sanz-Cortiella R, Llorens J, Arnó J, Escolà A. 2021. 
Computers and Electronics in Agriculture 191, 106553. DOI: https://doi.org/10.1016/j.compag.2021.106553

PFuji-Size dataset: A collection of images and photogrammetry-derived 3D point clouds with ground truth annotations for Fuji apple detection and size estimation in field conditions 
Gené-Mola J, Sanz-Cortiella R, Rosell-Polo JR, Escolà A, Gregorio E. 2021.  
Data in brief 39, 107629. DOI: https://doi.org/10.1016/j.dib.2021.107629

3D Spectral Graph Wavelet Point Signatures in Pre-Processing Stage for Mobile Laser Scanning Point Cloud Registration in Unstructured Orchard Environments 
Guevara J, Gené-Mola J, Gregorio E, Auat Cheein FA. 2021. 
IEEE Sensors Journal. DOI: https://doi.org/10.1109/JSEN.2021.3129340

Map-based zonal dosage strategy to control yellow spider mite (Eotetranychus carpini) and leafhoppers (Empoasca vitis & Jacobiasca lybica) in vineyards 
Román C, Arnó J, Planas S. 2021. 
Crop Protection 147 (2021), 105690. DOI: https://doi.org/10.1016/j.cropro.2021.105690

3D characterization of a Boston Ivy double-skin green building facade using a LiDAR system 
Pérez G, Escolà A, Rosell-Polo JR, Coma J, Arasanz R, Marrero B, Cabeza LF, Gregorio E. 2021. 
Building and Environment 206 (2021), 108320. DOI: https://doi.org/10.1016/j.buildenv.2021.108320

In-field apple size estimation using photogrammetry-derived 3D point clouds: Comparison of 4 different methods considering fruit occlusions 
Gené-Mola J, Sanz-Cortiella R, Rosell-Polo JR, Escolà A, Gregorio E. 2021. 
Computers and Electronics in Agriculture 188 (2021), 106343. DOI: https://doi.org/10.1016/j.compag.2021.106343

Comparison of 3D scan matching techniques for autonomous robot navigation in urban and agricultural environments 
Guevara J, Gené-Mola J, Gregorio E, Torres-Torriti M, Reina G, Auat Cheein FA. 2021. 
Journal of Applied Remote Sensing 16 (2), 024508. DOI: https://doi.org/10.1117/1.JRS.15.024508

Assessing the Performance of RGB-D Sensors for 3D Fruit Crop Canopy Characterization under Different Operating and Lighting Conditions 
Gené-Mola J, Llorens J, Rosell-Polo JR, Gregorio E, Arnó J, Solanelles F, Martínez-Casasnovas JA, Escolà A. 2020. 
Sensors, 20 (24), 7072. DOI: https://doi.org/10.3390/s20247072

Spatially variable pesticide application in vineyards: Part I, developing a geostatistical approach 
Del-Moral-Martínez I, Rosell-Polo JR, Uribeetxebarria A, Arnó J. 2020. 
Biosystems Engineering, 195 (2020), 17-26. DOI: https://doi.org/10.1016/j.biosystemseng.2020.04.014

Spatially variable pesticide application in vineyards: Part II, field comparison of uniform and map-based variable dose treatments 
Román C, Llorens J, Uribeetxebarria A, Sanz R, Planas S, Arnó J. 2020. 
Biosystems Engineering, 195 (2020), 42-53. DOI: https://doi.org/10.1016/j.biosystemseng.2020.04.013

Fuji-SfM dataset: A collection of annotated images and pointclouds for Fuji apple detection and location using structure-from-motion photogrammetry 
Gené-Mola J, Sanz-Cortiella R, Rosell-Polo JR, Morros JR, Ruiz-Hidalgo J, Vilaplana V, Gregorio E. 2020.
Data in brief, 29 (2020), 105591. DOI: https://doi.org/10.1016/j.dib.2020.105591

Analyzing and overcoming the effects of GNSS error on LiDAR based orchard parameters estimation 
Guevara J, Auat Cheein FA, Gené-Mola J, Rosell-Polo JR, Gregorio E. 2020.
Computers and Electronics in Agriculture, 170 (2020), 105255. DOI: https://doi.org/10.1016/j.compag.2020.105255

LFuji-air dataset: Annotated 3D LiDAR point clouds of Fuji apple trees for fruit detection scanned under different forced air flow conditions 
Gené-Mola J, Gregorio E, Auat F, Guevara J, Llorens J, Sanz-Cortiella R, Escolà A, Rosell-Polo JR. 2020.
Data in brief, 29 (2020), 105248. DOI: https://doi.org/10.1016/j.dib.2020.105248

Determination of spray drift and buffer zones in 3D crops using the ISO standard and new LiDAR methodologies 
Torrent X, Gregorio E, Rosell-Polo JR, Arnó J, Peris M, van de Zande J, Planas S. 2020. 
Science of the Total Environment, 714, 136666. DOI:  https://doi.org/10.1016/j.scitotenv.2020.136666

Fruit detection and 3D location using instance segmentation neural networks and structure-from-motion photogrammetry 
Gené-Mola J, Sanz-Cortiella R, Rosell-Polo JR, Morros JR, Ruiz-Hidalgo J, Vilaplana V, Gregorio E. 2020.
Computers and Electronics in Agriculture, 169 (2020), 105165. DOI: https://doi.org/10.1016/j.compag.2019.105165

Fruit detection, yield prediction and canopy geometric characterization using LiDAR with forced air flow 
Gené-Mola J, Gregorio E, Auat F, Guevara J, Llorens J, Sanz-Cortiella R, Escolà A, Rosell-Polo JR. 2020. 
Computers and Electronics in Agriculture, 168 (2020), 105121. DOI: https://doi.org/10.1016/j.compag.2019.105121

Detection of Lithologic Discontinuities in Soils: A Case Study of Arid and Semi-arid Regions of Iran
Esfandiarpour-Boroujeni I., Mosleh Z., Karimi A.R., Martínez-Casasnovas J.A. 2020
Eurasian Soil Science 53, 1374–1388. DOI: https://doi.org/10.1134/S1064229320100063

Geomorphic adjustments to multi-scale disturbances in a mountain river: A century of observations
Llena M., Vericat D., Martínez-Casasnovas J.A., Smith M.W. 2020
Catena 192, 104584. DOI: https://doi.org/10.1016/j.catena.2020.104584

Special issue on "Terrestrial laser scanning": Editor's notes 
Rosell-Polo JR, Gregorio E, Llorens J. 2019. 
Sensors, 19 (20), 4569. DOI: https://doi.org/10.3390/s19204569

Fruit detection in an apple orchard using a mobile terrestrial laser scanner 
Gené-Mola J, Gregorio E, Guevara J, Auat F, Sanz-Cortiella R, Escolà A, Llorens J, Morros JR, Ruiz-Hidalgo J, Vilaplana V, Rosell-Polo JR. 2019. 
Biosystems Engineering, 187 (2019), 171-184. DOI: https://doi.org/10.1016/j.biosystemseng.2019.08.017

Assessing ranked set sampling and ancillary data to improve fruit load estimates in peach orchards 
Uribeetxebarria A, Martínez-Casasnovas JA, Tisseyre B, Guillaume S, Escolà A, Rosell JR, Arnó J. 2019. 
Computers and Electronics in Agriculture, 164. DOI: https://doi.org/10.1016/j.compag.2019.104931

KFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data 
Gené-Mola J, Vilaplana V, Rosell-Polo JR, Morros JR, Ruiz-Hidalgo J, Gregorio E. 2019. 
Data in brief, 25 (2019), 104289. DOI: https://doi.org/10.1016/j.dib.2019.104289

Assessment of spray drift potential reduction for hollow-cone nozzles: Part 1. Classification using indirect methods 
Torrent X, Gregorio E, Douzals JP, Tinet C, Rosell-Polo JR, Planas S. 2019. 
Science of the Total Environment 692, 1322-1333. DOI: https://doi.org/10.1016/j.scitotenv.2019.06.121

Assessment of spray drift potential reduction for hollow-cone nozzles: Part 2. LiDAR technique 
Gregorio E, Torrent X, Planas S, Rosell-Polo JR. 2019. 
Science of the Total Environment 687, 967-977. DOI: https://doi.org/10.1016/j.scitotenv.2019.06.151

Spatial variability in commercial orange groves. Part 2: relating canopy geometry to soil attributes and historical yield 
Colaço A F, Molin J P, Rosell-Polo J R, Escolà A. 2018.
Precision Agriculture 20(4), 805-822. DOI: https://doi.org/10.1007/s11119-018-9615-0

Spatial variability in commercial orange groves. Part 1: canopy volume and height 
Colaço A F, Molin J P, Rosell-Polo J R, Escolà A. 2018.
Precision Agriculture 20(4), 788-804. DOI: https://doi.org/10.1007/s11119-018-9612-3

Multi-modal deep learning for Fuji apple detection using RGB-D cameras and their radiometric capabilities 
Gené-Mola J, Vilaplana V, Rosell-Polo JR, Morros JR, Ruiz-Hidalgo J, Gregorio E. 2019. 
Computers and Electronics in Agriculture 162, 689-698. DOI: https://doi.org/10.1016/j.compag.2019.05.016

Usability analysis of scan matching techniquesfor localization of field machinery in avocado groves 
Auat Cheein F, Torres-Torriti M, Rosell JR. 2019. 
Computers and Electronics in Agriculture 162. DOI: https://doi.org/10.1016/j.compag.2019.05.024

Stratified sampling in fruit orchards using cluster-based ancillary information maps: a comparative analysis to improve yield and quality estimates 
Uribeetxebarria A, Martínez-Casasnovas JA, Escolà A, Rosell JR, Arnó J. 2019.
Precision Agriculture 20(2), 179-192. DOI: https://doi.org/10.1007/s11119-018-9619-9

Nitrogen management in double-annual cropping system (barley-maize) under irrigated Mediterranean environments
Maresma Á., Martínez-Casasnovas J.A., Santiveri F., Lloveras J. 2019
European Journal of Agronomy 103, 98-107. DOI: https://doi.org/10.1016/j.eja.2018.12.002

First attempts to obtain a reference drift curve for traditional olive grove's plantations following ISO 22866
Gil, E.; Llorens, J.; Gallart, M.; Gil-Ribes, J.A.; Miranda-Fuentes, A.; 2018.
Science of The Total Environment 627, 349-360. DOI: https://doi.org/10.1016/j.scitotenv.2018.01.229

Application of light detection and ranging and ultrasonic sensors to high-throughput phenotyping and precision horticulture: current status and challenges 
Colaço A F, Molin J P, Rosell-Polo J R, Escolà A. 2018.
Horticulture Research 5 (1), 35-46. DOI: https://doi.org/10.1038/s41438-018-0043-0

Aplicación de algoritmos Structure from Motion (SfM) para el análisis histórico de cambios en la geomorfología fluvial 
Llena M, Vericat D, Martínez-Casanovas JA. 2018.
Cuaternario y Geomorfología (2018), 32 (1-2), 53-73. DOI: https://doi.org/10.17735/cyg.v31i3-4.55240

LIDAR and non-LIDAR-based canopy parameters to estimate the leaf area in fruit trees and vineyard 
Ricardo Sanz, Jordi Llorens, Alexandre Escolà, Jaume Arnó, Santiago Planas, Carla Román, Joan R. Rosell-Polo. 2018.
Agricultural and Forest Meteorology 260/261, 229-239. DOI: https://doi.org/10.1016/j.agrformet.2018.06.017

Use of Farmer Knowledge in the Delineation of Potential Management Zones in Precision Agriculture: A Case Study in Maize (Zea mays L.).  
Martínez-Casasnovas JA, Escolà A, Arnó J. 2018. 
Agriculture 2018, 8(6), 84. DOI: https://doi.org/10.3390/agriculture8060084

Polarization lidar detection of agricultural aerosol emissions 
Gregorio E, Gené J, Sanz R, Rocadenbosch F, Chueca P, Arnó J, Solanelles F, Rosell-Polo JR. 2018. 
Journal of Sensors 2018, 1864106. DOI: https://doi.org/10.1155/2018/1864106

Spatial variability in orchards after land transformation: Consequences for precision agriculture practices
Uribeetxebarria, A., Daniele, E., Escolà, A., Arnó, J., Martínez-Casasnovas, J.A., 2018.
Science of the Total Environment 635, 343–352. DOI: https://doi.org/10.1016/j.scitotenv.2018.04.153

Mechatronic terrestrial LiDAR for canopy porosity and crown surface estimation
Sebastián Arriagada Pfeiffer, Javier Guevara, Fernando Auat Cheein, Ricardo Sanz. 2018.
Computers and Electronics in Agriculture 146 (2018), 104-113. DOI: https://doi.org/10.1016/j.compag.2018.01.022

Use of Multispectral Airborne Images to Improve In-Season Nitrogen Management, Predict Grain Yield and Estimate Economic Return of Maize in Irrigated High Yielding Environments.
Maresma A. , Lloveras J. , Martínez-Casasnovas J. A. 2018.
Remote Sensing, 10(4), 543. DOI: https://doi.org/10.3390/rs10040543

Predicting soil water content at − 33 kPa by pedotransfer functions in stoniness soils in northeast Venezuela.  
Pineda M. C. , Viloria J. , Martínez-Casasnovas J. A., Valera A., Lobo D., Timm L.C., Pires L. F. , Gabriels D. 2018.
Environmental Monitoring and Assessment 190, 161: 1-11. DOI: https://doi.org/10.1007/s10661-018-6528-3

Apparent electrical conductivity and multivariate analysis of soil properties to assess soil constraints in orchards affected by previous parcelling
Uribeetxeberria A, Arnó J, Escolà A, Martínez-Casasnovas JA. 2018.
Geoderma 319, 185-193. DOI: https://doi.org/10.1016/j.geoderma.2018.01.008

Terrain classification using ToF sensors for the enhancement of agricultural machinery traversability 
Yandun Narváez F, Gregorio E, Escolà A, Rosell-Polo JR, Torres-Torriti M, Auat Cheein F. 2018. 
Journal of Terramechanics 76: 1-13. DOI: https://doi.org/10.1016/j.jterra.2017.10.005

Kinect v2 Sensor-Based Mobile Terrestrial Laser Scanner for Agricultural Outdoor Applications 
Rosell-Polo JR, Gregorio E, Gené J, Llorens J, Torrent X, Arnó J, Escolà A. 2017.
IEEE/ASME Transactions on Mechatronics 22(6), 2420-2427. DOI: https://doi.org/10.1109/TMECH.2017.2663436

Assessing opportunities for selective winery vintage with a market-driven composite index  
Arnó J, Martínez-Casasnovas JA. 2017. 
Cogent Food & Agriculture 3: 1386438. DOI: https://doi.org/10.1080/23311932.2017.1386438

Using centers of pressure tracks of sows walking on a large force platform in farm conditions for locomotion classification  
Puigdomenech Ll, Rosell-Polo JR, Blanco G, Babot D. 2017. 
Computers and Electronics in Agriculture 142, Part A , 101-109. DOI: https://doi.org/10.1016/j.compag.2017.08.022 

A Method to Obtain Orange Crop Geometry Information Using a Mobile Terrestrial Laser Scanner
and 3D Modeling
Colaço, A.F., Trevisan, R.G., Molin, J.P., Rosell-Polo, J.R., Escolà, A. 2017.
Remote Sensing 9(8), 763. DOI: https://doi.org/10.3390/rs9080763

Flexible system of multiple RGB-D sensors for measuring and classifying in agri-food industry 
Méndez Perez, R., Auat Cheein, F., Rosell-Polo, J.R. 2017. 
Computers and Electronics in Agriculture 139, 231-242. DOI: https://doi.org/10.1016/j.compag.2017.05.014

Understanding soil erosion processes in Mediterranean sloping vineyards (Montes de Málaga, Spain)
Rodrigo Comino J, Senciales JM, Ramos MC, Martínez-Casasnovas JA, Lasanta T, Brevik EC, Ries JB, Ruiz Sinoga JD. 2017.
Geoderma 296, 47-59. DOI: https://doi.org/10.1016/j.geoderma.2017.02.021

Comparison between standard and drift reducing nozzles for pesticide application in citrus: Part I. Effects on wind tunnel and field spray drift
Torrent, X., Garcerá, C., Moltó, E., Chueca, P., Abad, R., Grafulla, C., Román, C., Planas, S., 2017.
Crop Protection 96, 130–143. DOI: https://doi.org/10.1016/j.cropro.2017.02.001

Setting the optimal length to be scanned in rows of vines by using mobile terrestrial laser scanners
Arnó J, Escolà A, Rosell-Polo JR. 2017.
Precision Agriculture 18, 145-151. DOI: https://doi.org/10.1007/s11119-016-9451-z

Mobile terrestrial laser scanner applications in precision fruticulture/horticulture and tools to extract information from canopy point clouds
Escolà A, Martínez-Casasnovas JA, Rufat J, Arnó J, Arbonés A, Sebé F, Pascual M, Gregorio E, Rosell-Polo JR. 2017.
Precision Agriculture 18, 111-132. DOI: https://doi.org/10.1007/s11119-016-9474-5

Analysis of Vegetation Indices to Determine Nitrogen Application and Yield Prediction in Maize (Zea mays L.) from a Standard UAV Service 
Maresma Á, Ariza M, Martínez E, Lloveras J, Martínez-Casasnovas JA. 2016.
Remote Sensing 8, 973-976. DOI: https://doi.org/10.3390/rs8120973

Database extension for digital soil mapping using artificial neural networks 
Bagheri Bodaghabadi M, Martínez-Casasnovas JA, Esfandiarpour Borujeni I, Salehi MH, Mohammadi J, Toomanian N. 2016.
Arabian Journal of Soil Sciences 701, 1-13. DOI: https://doi.org/10.1007/s12517-016-2732-z

Landslides susceptibility change over time according to terrain conditions in a mountain area of the tropic region 
Pineda MC, Viloria J, Martínez-Casasnovas JA. 2016.
Environmental Monitoring and Assessment 188, 1-12. DOI: https://doi.org/10.1007/s10661-016-5240-4

Relación entre los cambios de cobertura vegetal y la ocurrencia de deslizamientos de tierra en la Serranía del Interior, Venezuela 
Pineda MC, Martínez-Casasnovas JA, Viloria J. 2016.
Interciencia 41, 190-197. http://hdl.handle.net/10459.1/60175

Soil and Water Assessment Tool Soil Loss Simulation at the Sub-Basin Scale in the Alt Penedes-Anoia Vineyard Region (Ne Spain) in the 2000s 
Martínez-Casasnovas JA, Ramos MC, Benites GC. 2016.
Land Degradation and Development 27, 160-170. DOI: https://doi.org/10.1002/ldr.2240

A LiDAR-Based System to Assess Poplar Biomass 
Andújar D, Escolà A, Rosell-Polo JR, Sanz R, Rueda-Ayala V, Fernández-Quintanilla C, Ribeiro A, Dorado J. 2016.
Gesunde Pflanzen 68, 155-162. DOI: https://doi.org/10.1007/s10343-016-0369-1

Multi-tree woody structure reconstruction from mobile terrestrial laser scanner point clouds based on a dual neighbourhood connectivity graph algorithm 
Valeriano Méndez, Joan R. Rosell-Polo, Miquel Pascual, Alexandre Escolà. 2016.
Biosystems Engineering 148, 34-47. DOI: https://doi.org/10.1016/j.biosystemseng.2016.04.013

Measurement of Spray Drift with a Specifically Designed Lidar System 
Eduard Gregorio, Xavier Torrent, Santiago Planas de Martí, Francesc Solanelles, Ricarso Sanz, Francesc Rocadenbosch, Joan Masip, Manel Ribes-Dasi, Joan R. Rosell-Polo. 2016.
Sensors 16(4) paper 499. DOI: https://doi.org/10.3390/s16040499

Algebraic path tracking to aid the manual harvesting of olives using an automated service unit 
Fernando A. Auat Cheein, Gustavo Scaglia, Miguel Torres-Torriti, José Guivant, Alvaro Javier Prado, Jaume Arnó, Alexandre Escolà, Joan R. Rosell-Polo JR. 2016.
Biosystems Engineering 142, 117-132. DOI: https://doi.org/10.1016/j.biosystemseng.2015.12.006

Lidar: Towards a new methodology for field measurement of spray drift 
Gregorio E; Torrent X; Solanelles F; Sanz R; Rocadenbosch F; Masip J; Ribes-Dasi M; Planas S; Rosell-Polo JR. 2016.
Aspects of Applied Biology 132, 201-206. http://hdl.handle.net/10459.1/49480

Mapping Vineyard Leaf Area Using Mobile Terrestrial Laser Scanners: Should Rows be Scanned On-the-Go or Discontinuosly Sampled? 
Ignacio del-Moral-Martínez, Joan R. Rosell-Polo, Joaquim Company, Ricardo Sanz, Alexandre Escolà, Joan Masip, José A. Martínez-Casasnovas and Jaume Arnó. 2016.
Sensors 16(1), 119, pp. 1-13. DOI: https://doi.org/10.3390/s16010119

Precision feeding can significantly reduce lysine intake and nitrogen excretion without compromising the performance of growing pigs
Andretta I., Pomar C., Rivest J., Pomar J., Radünz J. 2016
Animal 10 (7), 1137-1147. DOI: https://doi.org/10.1017/S1751731115003067

Effect of a lysine depletion-repletion protocol on the compensatory growth of growing-finishing pigs
Cloutier L., Létourneau-Montminy M.P., Bernier J.F., Pomar J., Pomar C. 2016
Journal of Animal Science 94 (1), 255–266. DOI: https://doi.org/10.2527/jas.2015-9618

Testing the suitability of a terrestrial 2D LiDAR scanner for canopy characterization of greenhouse tomato crops
Llop J., Gil E., Llorens J., Miranda-Fuentes A., Gallart M. 2016
Sensors (Switzerland)16(9), 1435. DOI: https://doi.org/10.3390/s16091435

Assessing the optimal liquid volume to be sprayed on isolated olive trees according to their canopy volumes
Miranda-Fuentes A., Llorens J., Rodríguez-Lizana A., Cuenca A., Gil E., Blanco-Roldán G.L., Gil-Ribes J.A. 2016
Science of the Total Environment 568, 296-305. DOI: https://doi.org/10.1016/j.scitotenv.2016.06.013

Real-time approaches for characterization of fully and partially scanned canopies in groves 
Auat Cheein F, Guivant J, Sanz R, Escolà A, Yandún F, Torres-Torriti M, Rosell-Polo JR. 2015.
Computers and Electronics in Agriculture 118, 361-371. DOI: https://doi.org/10.1016/j.compag.2015.09.017

Assessment of the FAO traditional land evaluation methods. A casestudy: Iranian Land Classification method 
Bagheri Bodaghabadi M, Martínez-Casasnovas JA, Khakili P, Masihabadi MH, Gandomkar A. 2015.
Soil Use and Management. In Press. DOI: https://doi.org/10.1111/sum.12191

Vine vigor, yield and grape quality assessment by airborne remote sensing over three years: Analysis of unexpected relationships in cv. Tempranillo 
Bonilla I, Martínez de Toda F, Martínez-Casasnovas JA. 2015.
Spanish Journal of Agricultural Research 13(2): e0903. DOI: https://doi.org/10.5424/sjar/2015132-7809

Digital Soil Mapping usin Artificial Neuronal Networks (ANN) and Terrain-Modelling Attributes 
Bagheri Bodaghabadi M, Martínez-Casasnovas JA, Salehi MH, Mohammadi J, Esfandiarpoor Borujeni I, Toomanian N, Gandomkar A. 2015.
Pedosphere 25(4): 580-591. https://doi.org/10.1016/S1002-0160(15)30038-2

Advances in Structured Light Sensors Applications in Precision Agriculture and Livestock Farming 
Rosell-Polo JR; Auat Cheein F; Gregorio E; Andújar D; Puigdomènech L; Masip J; Escolà A. 2015.
Advances in Agronomy 133: 71-112. DOI: https://doi.org/10.1016/bs.agron.2015.05.002

Soil water content, runoff and soil loss prediction in a small ungauged agricultural basin in the Mediterranean region using the Soil and Water Assessment Tool 
Ramos MC; Martínez-Casasnovas JA. 2015.
Journal of Agricultural Science 153: 481-496. DOI: https://doi.org/10.1017/S0021859614000422

Georeferenced Scanning System to Estimate the Leaf Wall Area in Tree Crops 
del-Moral-Martínez I; Arnó J; Escolà A; Masip J; Sanz R; Masip-Vilalta J; Company-Mesa J; Rosell-Polo JR. 2015.
Sensors 15(4): 8382-8405. DOI: https://doi.org/10.3390/s150408382

Influence of the scanned side of the row in terrestrial laser sensor applications in vineyards: practical consequences
Arnó J; Escolà A; Masip J; Rosell-Polo JR. 2015.
Precision Agriculture 16, 119-128. DOI: https://doi.org/10.1007/s11119-014-9364-7

Eye-Safe Lidar System for Pesticide Spray Drift Measurement
Gregorio E; Rocadenbosch F; Sanz R; Rosell-Polo JR. 2015.
Sensors 15(2): 3650-3670. DOI: https://doi.org/10.3390/s150203650

Unexpected relationships between vine vigor and grape composition in warm climate conditions
Bonilla I., De Toda F.M., Martínez-Casasnovas J.A. 2015.
Journal International des Sciences de la Vigne et du Vin 49(2), 127–136. DOI: https://doi.org/10.20870/oeno-one.2015.49.2.87

Evaluation of a method estimating real-time individual lysine requirements in two lines of growing-finishing pigs
Cloutier L., Pomar C., Létourneau Montminy M.P., Bernier J.F., Pomar J. 2015.
Animal 9(4):561-568. DOI: https://doi.org/10.1017/S1751731114003073

Deciduous tree reconstruction algorithm based on cylinder fitting from mobile terrestrial laser scanned point clouds
Méndez V; Rosell-Polo JR; Sanz R; Escolà A; Catalán, H. 2014.
Biosystems Engineering 124: 78-88. DOI: https://doi.org/10.1016/j.biosystemseng.2014.06.001

LIDAR as an alternative to passive collectors to measure pesticide spray drift 
Gregorio E; Rosell-Polo JR; Sanz R; Rocadenbosch F; Solanelles F; Garcerá C; Chueca P; Arnó J; del Moral I; Masip J; Camp F; Viana R; Escolà A; Gràcia F; Planas S; Moltó E. 2014.
Atmospheric Environment 82: 83-93. DOI: https://doi.org/10.1016/j.atmosenv.2013.09.028

Advanced Technologies for the Improvement of Spray Application Techniques in Spanish Viticulture:
An Overview 
Gil E; Arnó J; Llorens J; Sanz R; Llop J; Rosell-Polo JR; Gallart M; Escolà A. 2014.
Sensors 14(1): 691-708. DOI: https://doi.org/10.3390/s140100691

The impact of feeding growing–finishing pigs with daily tailored diets using precision feeding techniques on animal performance, nutrient utilization, and body and carcass composition 
I. Andretta, C. Pomar, J. Rivest, J. Pomar, P. A. Lovatto and J. Radünz Neto. 2014.
Journal of Animal Science, 92(9), 3925 - 3936. DOI: https://doi.org/10.2527/jas.2014-7643. ISSN: 0021-8812

The impact of daily multiphase feeding on animal performance, body composition, nitrogen and phosphorus excretions, and feed costs in growing–finishing pigs
C. Pomar, J. Pomar; F. Dubeau; E. Joannopoulos; J.-P. Dussault. 2014.
Animal, 8(5), 704 - 713. DOI: https://doi.org/10.1017/S1751731114000408. ISSN: 1751-7311

Discriminating Crop, Weeds and Soil Surface with a Terrestrial LIDAR Sensor 
Andújar D; Rueda-Ayala V; Moreno H; Rosell-Polo JR; Escolà A; Valero C; Gerhards R; Fernández-Quintanilla C; Dorado J; Griepentrog HW. 2013.
Sensors 13(11):14662-14675. DOI: https://doi.org/10.3390/s131114662

Variable rate sprayer. Part 1 – Orchard prototype: Design, implementation and validation 
Escolà A; Rosell-Polo JR; Planas S; Gil E; Pomar J; Camp F; Llorens J; Solanelles F. 2013.
Computers and Electronics in Agriculture 95:122-135. DOI: https://doi.org/10.1016/j.compag.2013.02.004

Variable rate sprayer. Part 2 – Vineyard prototype: Design, implementation, and validation 
Gil E; Llorens J; Llop J; Fàbregas X; Escolà A; Rosell-Polo JR. 2013.
Computers and Electronics in Agriculture 95:136-150. DOI: https://doi.org/10.1016/j.compag.2013.02.010

LiDAR simulation in modelled orchards to optimise the use of terrestrial laser scanners and derived vegetative measures
Méndez V., Catalán H., Rosell-Polo J.R., Arnó J., Sanz R. 2013.
Biosystems Engineering 115, 7-19. DOI: https://doi.org/10.1016/j.biosystemseng.2013.02.003

Potential of a terrestrial LiDAR-based system to characterise weed vegetation in maize crops 
Andújar D, Escolà A, Rosell-Polo JR, Fernández-Quintanilla C, Dorado J. 2013.
Computers and Electronics in Agriculture 92:11-15. DOI: https://doi.org/10.1016/j.compag.2012.12.012

Leaf area index estimation in vineyards using a ground-based LiDAR scanner 
Arnó J, Escolà A, Vallès JM, Llorens J, Sanz R, Masip J, Palacín J, Rosell JR. 2013.
Precision Agriculture 14, 290-306. DOI: https://doi.org/10.1007/s11119-012-9295-0

Relationship between tree row LIDAR-volume and leaf area density for fruit orchards and vineyards obtained with a LIDAR 3D Dynamic Measurement System 
Sanz R., Rosell J.R., Llorens J., Gil E., Planas S. 2013.
Agricultural and Forest Meteorology 171/172, 153-162. DOI: https://doi.org/10.1016/j.agrformet.2012.11.013

Effet d'un protocole de déplétion-réplétion en lysine chez le porc en croissance 
Cloutier, L. ; Létourneau M.P. ; Bernier, J.; Pomar, J.; Pomar, C. 2013.
Journées Recherche Porcine 45(1), 149 - 154. ISSN: 0767-9874

Lack of anisotropic effects in the spatial distribution of Cydia pomonella pheromone trap catches in Catalonia, NE Spain 
Comas C, Avilla J, Sarasúa MJ, Albajes R, Ribes-Dasi M. 2012.
Crop Protection 34:88-95. DOI: https://doi.org/10.1016/j.cropro.2011.12.005

Backscatter error bounds for the elastic lidar two-component inversion algorithm
Rocadenbosch, F., Frasier, S., Kumar, D., Lange Vega, D., Gregorio, E., Sicard, M. 2012.
IEEE Transactions on Geoscience and Remote Sensing 50(11),4791-4803. DOI: https://doi.org/10.1109/TGRS.2012.2194501

Parameter design of a biaxial lidar ceilometer
Gregorio, E., Rocadenbosch, F., Tiana-Alsina, J., Comerón, A., Sanz, R., Rosell-Polo, J.R. 2012.
Journal of Applied Remote Sensing 6, 063546. DOI: https://doi.org/10.1117/1.JRS.6.063546

Analysis of vineyard differential management zones and relation to vine development, grape maturity and quality 
Martinez-Casasnovas, J.A., Agelet-Fernandez, J., Arno, J., Ramos, M.C. 2012.
Spanish Journal of Agricultural Research  10(2): 326-337. DOI: https://doi.org/10.5424/sjar/2012102-370-11

A review of methods and applications of the geometric characterization of tree crops in agricultural activities
Rosell, J.R., Sanz, R. 2012.
Computers and Electronics in Agriculture 81, 124-141. DOI: https://doi.org/10.1016/j.compag.2011.09.007

Spatial variability in grape yield and quality influenced by soil and crop nutrition characteristics 
Arnó, J., Rosell, J.R., Blanco, R., Ramos, M.C., Martínez-Casasnovas, J.A. 2012.
Precision Agriculture 13, 393-410. DOI: https://doi.org/10.1007/s11119-011-9254-1

SIMLIDAR- Simulation of LIDAR performance in artificially simulated orchards
Méndez, V., Catalán, H., Rosell, J.R., Arnó, J., Sanz, R., Tarquis, A. 2012.
Biosystems Engineering 111(1), 72-82 . DOI: https://doi.org/10.1016/j.biosystemseng.2011.10.010
 
L’alimentation de précision chez le porc charcutier : Estimation des niveaux dynamiques de lysine digestible nécessaires à la maximisation du gain de poids
Zhang, G.; Pomar, C.; Pomar, J.; Del Castillo, J. 2012.
Journées Recherche Porcine , 44(1), 171 - 176. ISSN: 0767-9874.
 
Development of sustainable precision farming systems for swine: Estimating real-time individual energy and nutrient requirements in growing-finishing pigs
L. Hauschild; P. A. Lovatto; J. Pomar; C. Pomar 2012.
Journal of Animal Science, 78(1), 88 - 97.DOI: https://doi.org/10.2527/jas.2011-4252

Agent-based simulation framework for virtual prototyping of advanced livestock precision feeding systems
Pomar J., López V., Pomar C. 2011.
Computers and Electronics in Agriculture 78, 88-97. DOI: https://doi.org/10.1016/j.compag.2011.06.004

Characterisation of the LMS200 laser beam under the influence of blockage surfaces. Influence on 3D scanning of tree orchards
Sanz, R., Llorens, J., Rosell, J.R., Gregorio, E., Palacín, J. 2011.
Sensors 11(3), 2751-2772. DOI: https://doi.org/10.3390/s110302751

Innovative LIDAR 3D dynamic measurement system to estímate fruit-tree leaf area 
Sanz, R., Llorens, J., Escolà, A., Arnó, J., Ribes, M., Masip, J., Camp, F., Gràcia, F., Solanelles, F., Planas, S., Pallejà, T., Palacín, J., Gregorio, E., Del-Moral, I., Rosell, J.R. 2011.
Sensors 11(6), 5769-5791. DOI: https://doi.org/10.3390/s110605769

Performance of an Ultrasonic Ranging Sensor in Apple Tree Canopies. 
Escolà, A., Planas, S., Rosell, J.R., Pomar, J., Camp, F., Solanelles, F., Gràcia, F., Llorens, J., Gil, E. 2011.
Sensors 11(3), 2459-2477. DOI: https://doi.org/10.3390/s110302459

Ultrasonic and LIDAR Sensors for Electronic Canopy Characterization in Vineyards: Advances to Improve Pesticide Application Methods 
Llorens, J., Gil, E, Llop, J., Escolà, A. 2011.
Sensors 11(2), 2177-2194. DOI: https://doi.org/10.3390/s110202177
Guanyador del 4rt Premi de la revista Sensors al Sensors Best Paper Award 2015!   

Weed discrimination using ultrasonic sensors.
Andújar, D., Escolà, A., Dorado, J., Fernández-Quintanilla, C. 2011.
Weed Research 51(6), 543-547. DOI: https://doi.org/10.1111/j.1365-3180.2011.00876.x
 

Evaluation of peach tree growth characteristics under different irrigation strategies by LIDAR system: preliminary results 
Pascual, M., Villar, J.M., Rufat, J. Rosell, J.R. Sanz, R., Arnó, J. 2011.
Acta Horticulturae (ISHS) 889, 227-232. DOI: https://doi.org/10.17660/ActaHortic.2011.889.26
 
Clustering of grape yield maps to delineate site-specific management zones   
Arno, J.,  Martinez-Casasnovas, J.A., Ribes-Dasi, M., Rosell, J.R. 2011.
Spanish Journal of Agricultural Research  9(3): 721-729. DOI: https://doi.org/10.5424/sjar/20110903-456-10

Liquid distribution of air induction and off-center spray nozzles under different conditions 
Viana, R.G., Ferreira, L.R., Rosell, J.R., Solanelles, F., Fillat, A., Machado, M .S.,  Machado, A.F.L., Silva, M.C.C. 2010.
Planta Daninha  28(2): 429-473. DOI: https://doi.org/10.1590/s0100-83582010000200023
 
Volumetric distribution and droplet spectrum by low drift spray nozzles 
Viana, R.G., Ferreira, L.R., Ferreira, M.C., Teixeira, M.M. Rosell, J.R., Santos, L.D.T., Machado, A.F.L. 2010.
Planta Daninha  28(2): 439-446. DOI: https://doi.org/10.1590/s0100-83582010000200024
 
Sensitivity of tree volume measurement to trajectory errors from a terrestrial LIDAR scanner 
Palleja, T., Tresanchez, M., Teixido, M.,; et al. Sanz, R., Rosell, J.R., Palacín, J. 2010.
Agricultural and Forest Meteorology  150(11), 1420-1427. DOI: https://doi.org/10.1016/j.agrformet.2010.07.005 

Variable rate dosing in precision viticulture: Use of electronic devices to improve application efficiency
Llorens J, Gil E, Llop J, Escolà A. 2010.
Crop Protection 29(3), 239-248. DOI: https://doi.org/10.1016/j.cropro.2009.12.022

Protocolo para la zonificación intraparcelaria de la viña para vendimia selectiva a partir de imágenes multiespectrales
Martínez-Casasnovas, J.A., Agelet, J., Arnó, J., Bordes, X., Ramos, M.C. 2010.
Revista de Teledetección, 33, 47-52. http://hdl.handle.net/10459.1/46421

Applying precision feeding techniques in growing-finishing pig operations
Pomar C., Hauschild L., Zhang G.H., Pomar J., Lovatto P.A. 2009.
Revista Brasileira de Zootecnia 38, 226-237. DOI: https://doi.org/10.1590/S1516-35982009001300023

A tractor-mounted scanning LIDAR for the non-destructive measurement of vegetative volume and surface area of tree-row plantations: a comparison with conventional destructive measurements 
Rosell Polo, J. R., Sanz, R., Llorens, J., Arnó, J., Escolà, A., Ribes-Dasi, M., Masip, J. Camp, F., Gràcia, F., Solanelles, F., Pallejà, T., Val, L., Planas, S., Gil, E., Palacín, J. 2009.
Biosystems Engineering 102(2), 128-134 . DOI: https://doi.org/10.1016/j.biosystemseng.2008.10.009 
 
Obtaining the three-dimensional structure of tree orchards from remote 2D terrestrial LIDAR scanning 
Rosell, J.R., Llorens, J., Sanz, R., Arnó, J., Ribes-Dasi, M., Masip, J., Escolà, A., Camp, F., Solanelles, F., Gràcia, F., Gil, E., Val, L., Planas, S., Palacín, J. 2009.
Agricultural and Forest Meteorology 149(9), 1505-1515. DOI: https://doi.org/10.1016/j.agrformet.2009.04.008
 
Design of a decision support method to determine volume rate for vineyard spraying 
Gil, E., Escolà, A. 2009.
Applied Engineering in Agriculture 25(2), 145-151. DOI: https://doi.org/10.13031/2013.26323

Review. Precision viticulture. Research topics, challenges and opportunities in site-specific vineyard management 
Arnó, J., Martínez-Casasnovas, J.A., Ribes-Dasi, M., Rosell, J.R. 2009.
Spanish Journal of Agricultural Research 7(4), 779-790. DOI: https://doi.org/10.5424/sjar/2009074-1092
 
The transversal spray deposition of double plain spurt nozzles TTJ60-11004 and TTJ60-11002 in different operational conditions 
Viana, R.G., Ferreira, L.R., Rosell, J.R., Solanelles, F., Planas, S., Machado, M .S.,  Machado, A.F.L. 2009.
Planta Daninha  27(2): 397-403. DOI: https://doi.org/10.1590/s0100-83582009000200024

Real-time tree-foliage surface estimation using a ground laser scanner 
Palacín, J., Pallejà, T., Tresanchez, M., Sanz, R., Llorens, J., Ribes-Dasi, M., Masip, J., Arnó, J., Escolà, A., Rosell, J.R. 2007.
IEEE Transactions on Instrumentation and Measurement 56(4), 1377-1383. DOI: https://doi.org/10.1109/TIM.2007.900126

Variable rate application of plant protection products in vineyard using ultrasonic sensors 
Gil, E., Escolà, A., Rosell, J.R., Planas, S., Val, L. 2007.
Crop Protection 26(8), 1287-1297. DOI: https://doi.org/10.1016/j.cropro.2006.11.003

An electronic control system for pesticide application proportional to the canopy with of tree crops 
Solanelles, F. Escolà, A., Planas, S., Rosell, J.R., Camp, F., Gràcia, F. 2006.
Biosystems Engineering 95(4), 473-481 . DOI: https://doi.org/10.1016/j.biosystemseng.2006.08.004

A mathematical model for designing and sizing sow farms
Plà L.M., Babot D., Pomar J. 2004.
International Transactions in Operational Research 11 (5), 485-494. DOI: https://doi.org/10.1111/j.1475-3995.2004.00472.x

A sow herd decision support system based on an embedded Markov model
Plà L.M., Pomar C., Pomar J. 2004.
Computers and Electronics in Agriculture 45 (1–3), 51-69. DOI: https://doi.org/10.1016/j.compag.2004.06.005

A Markov decision sow model representing the productive lifespan of herd sows
Plà L.M., Pomar C., Pomar J. 2003.
Agricultural Systems 76 (1), 253-272. DOI: https://doi.org/10.1016/S0308-521X(02)00102-6