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A seed rain community in a reforested post-agricultural field and adjacent secondary forest of Mount Papandayan Nature Reserve,West Java,Indonesia

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Journal of Forestry Research 2021年第3期32卷 1013-1023页

作者： nuri nurlaila setiawan Endah Sulistyawati School of Life Sciences and Technology Labtek XIInstitut Teknologi BandungJI.Ganesha 10Bandung 40132Indonesia

Understanding the mechanisms and barriers to the restoration of degraded land,especially post agriculture,will help provide protocols on effective ways of restoration into functional *** of the barriers in early stages of forest restoration is the arrival and availability of *** rain and factors affecting it(i.e.,distance to the forest edge,species diversity and surrounding vegetation)were measured in a reforested post-agricultural field and in an adjacent secondary *** g lm analysis was used on the seed rain community data and univariate lm analysis on the most abundant seed captured(i.e.,Schima wallichii(DC.)Korth.).After 8 months of seed rain collection,there was a total of 3596 seeds from eight tree *** were more abundant and more diverse in the secondary forest(74.9%,8 species)compared to the reforested field(24.1%,2 species).There was a limitation on seed dispersal in reforested field from the adjacent *** abundance of *** seeds determined by the dominance of adult trees(136 trees/ha in the forest and 115 trees/ha in the reforested field).Our study suggests,that after 7 years of planting,the reforested field has received limited seed rain and has not yet recovered.

关键词： Seed rain Montane tropical forest Post-agriculture succession Forest edge Multivariate analysis

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Detectability of multi-dimensional movement and behaviour in cattle using sensor data and machine learning algorithms:Study on a updates Charolais bull

Artificial Intelligence in Agriculture

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Artificial Intelligence in Agriculture 2024年第4期14卷 86-98页

作者： Miklos Biszkup Gabor Vasarhelyi nuri nurlaila setiawan Aliz Marton Szilard Szentes Petra Balogh Barbara Babay-Torok Gabor Pajor Dora Drexler Hungarian Research Institute of Organic Agriculture BudapestHungary CollMot Robotics Ltd BudapestHungary Eotvos University Department of Biological PhysicsBudapestHungary University of Veterinary Medicine Animal BreedingNutrition and Laboratory Animal Science DepartmentBudapestHungary

The development of motion sensors for monitoring cattle behaviour has enabled farmers to predict the state of their cattle's welfare more *** most studies work with one dimensional output with disjunct behaviour categories,more accurate prediction can still be achieved by including complex movements and enriching the sensor algorithm to detect multi-dimensional movements,i.e.,more than one movement occurring *** paper presents such a machine-learning method for analysing overlapping independent *** output of the method consists of automatically recognized complex behaviour patterns that can be used for measuring animal welfare,predicting calving,or detecting early signs of *** study combines automated motion sensors(ie.,halter and pedometer)for ruminants known as RumiwWatch mounted on a Charolais fattening bull and camera *** types of complex movements were identified,ie.,defecating-urinating,eating,drinking,getting up,head movement,licking,lying down,lying,playingaggression,rubbing,ruminating,sleeping,standing,and *** multiple parallel binary classificators were used,the system was able to recognize parallel behavioural patterns with high *** types of machine learning,i.e.,Support Vector Classification(SvC)and RandomForest were used to recognize different general and non-general forms of *** from these two supervised learning systems were compared.A continuous forty-eight hours of video were annotated to train the systems and validate their *** successrate of both classifiers in recognizing special movements from both sensors or separately in different settings(i.e.,window and padding)was *** the two classifiers produced different results,the ideal settings showed that all forms of movement in the subject animal were successfully recognized with high *** studies using more individual animals and different ruminants would increase our

关键词： Cattle PLF Motion sensors RumiWatch Complex behaviour Multi-dimensional Parallel Machinelearning

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