scholarly journals Conditioning Machine Learning Models to Adjust Lowbush Blueberry Crop Management to the Local Agroecosystem

Plants ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1401 ◽  
Author(s):  
Serge-Étienne Parent ◽  
Jean Lafond ◽  
Maxime C. Paré ◽  
Léon Etienne Parent ◽  
Noura Ziadi
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Gaussian Processes ◽  
Compositional Data ◽  
Soil Test ◽  
Compositional Data Analysis ◽  
Total Precipitation ◽  
Flower Bud ◽  
Lowbush Blueberry ◽  
Bud Opening

Agroecosystem conditions limit the productivity of lowbush blueberry. Our objectives were to investigate the effects on berry yield of agroecosystem and crop management variables, then to develop a recommendation system to adjust nutrient and soil management of lowbush blueberry to given local meteorological conditions. We collected 1504 observations from N-P-K fertilizer trials conducted in Quebec, Canada. The data set, that comprised soil, tissue, and meteorological data, was processed by Bayesian mixed models, machine learning, compositional data analysis, and Markov chains. Our investigative statistical models showed that meteorological indices had the greatest impact on yield. High mean temperature at flower bud opening and after fruit maturation, and total precipitation at flowering stage showed positive effects. Low mean temperature and low total precipitation before bud opening, at flowering, and by fruit maturity, as well as number of freezing days (<−5 °C) before flower bud opening, showed negative effects. Soil and tissue tests, and N-P-K fertilization showed smaller effects. Gaussian processes predicted yields from historical weather data, soil test, fertilizer dosage, and tissue test with a root-mean-square-error of 1447 kg ha−1. An in-house Markov chain algorithm optimized yields modelled by Gaussian processes from tissue test, soil test, and fertilizer dosage as conditioned to specified historical meteorological features, potentially increasing yield by a median factor of 1.5. Machine learning, compositional data analysis, and Markov chains allowed customizing nutrient management of lowbush blueberry at local scale.

scholarly journals Adjusting Mineral Nutrition of Lowbush Blueberry to Agroecosystem Conditions

2020 ◽  
Author(s):  
Serge-Étienne Parent ◽  
Jean Lafond ◽  
Maxime Paré ◽  
Léon Etienne Parent ◽  
Noura ZIadi
Keyword(s):  
Machine Learning ◽  
Gaussian Processes ◽  
Nutrient Management ◽  
Total Precipitation ◽  
Learning Models ◽  
Flower Bud ◽  
Mean Temperature ◽  
Lowbush Blueberry ◽  
Bud Opening ◽  
Machine Learning Models

Nutrient management of lowbush blueberry (Vaccinium angustifolium Ait.) depends on several yield-limiting features. Machine learning models can process such yield-impacting variables to predict berry yield. We investigated the effects of local variables on yields and nutrient management of lowbush blueberry. We collected 1504 observations from N-P-K fertilizer trials conducted in Quebec, Canada. Meteorological indices at various phenological stages showed the greatest impact on yield. High mean temperature at flower bud opening and after fruit maturation, and total precipitation at flowering showed positive effects. Low mean temperature and low total precipitation before bud opening, at flowering, and by fruit maturity, as well as number of freezing days (&lt; -5&ordm;C) before flower bud opening, showed negative effects. Soil fertility variables, leaf nutrient compositions and N-P-K fertilization showed smaller effects. Gaussian processes predicted berry yields from historical weather data, soil analysis, fertilizer dosage, and leaf nutrients with a root-mean-square-error of 1447 kg ha-1 on the testing data set. An in-house Markov chain algorithm optimized yields modelled with Gaussian processes from leaf nutrient composition, soil test value, and fertilizer dosage conditioned to specified historical weather features. We propose to use conditioned machine learning models to manage nutrients of lowbush blueberry at local scale.

scholarly journals Effects of Two Randomized and Controlled Multi-Component Interventions Focusing On 24-Hour Movement Behavior among Office Workers: A Compositional Data Analysis

2021 ◽  
Vol 18 (8) ◽  
pp. 4191
Author(s):  
Lisa-Marie Larisch ◽  
Emil Bojsen-Møller ◽  
Carla F. J. Nooijen ◽  
Victoria Blom ◽  
Maria Ekblom ◽  
...  
Keyword(s):  
Physical Activity ◽  
Data Analysis ◽  
Cardiorespiratory Fitness ◽  
Leisure Time ◽  
Compositional Data ◽  
Office Workers ◽  
Movement Behavior ◽  
Compositional Data Analysis ◽  
Intervention Effects ◽  
Time In Bed

Intervention studies aiming at changing movement behavior have usually not accounted for the compositional nature of time-use data. Compositional data analysis (CoDA) has been suggested as a useful strategy for analyzing such data. The aim of this study was to examine the effects of two multi-component interventions on 24-h movement behavior (using CoDA) and on cardiorespiratory fitness among office workers; one focusing on reducing sedentariness and the other on increasing physical activity. Office workers (n = 263) were cluster randomized into one of two 6-month intervention groups, or a control group. Time spent in sedentary behavior, light-intensity, moderate and vigorous physical activity, and time in bed were assessed using accelerometers and diaries, both for 24 h in total, and for work and leisure time separately. Cardiorespiratory fitness was estimated using a sub-maximal cycle ergometer test. Intervention effects were analyzed using linear mixed models. No intervention effects were found, either for 24-h behaviors in total, or for work and leisure time behaviors separately. Cardiorespiratory fitness did not change significantly. Despite a thorough analysis of 24-h behaviors using CoDA, no intervention effects were found, neither for behaviors in total, nor for work and leisure time behaviors separately. Cardiorespiratory fitness did not change significantly. Although the design of the multi-component interventions was based on theoretical frameworks, and included cognitive behavioral therapy counselling, which has been proven effective in other populations, issues related to implementation of and compliance with some intervention components may have led to the observed lack of intervention effect.

scholarly journals The Gut Microbiota of Healthy Aged Chinese Is Similar to That of the Healthy Young

mSphere ◽  
2017 ◽  
Vol 2 (5) ◽  
Author(s):  
Gaorui Bian ◽  
Gregory B. Gloor ◽  
Aihua Gong ◽  
Changsheng Jia ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Gut Microbiota ◽  
Large Scale ◽  
Compositional Data ◽  
Healthy Lifestyle ◽  
Compositional Data Analysis ◽  
Surprising Result ◽  
Microbiota Composition ◽  
Cross Sectional ◽  
Age Cohorts

ABSTRACT We report the large-scale use of compositional data analysis to establish a baseline microbiota composition in an extremely healthy cohort of the Chinese population. This baseline will serve for comparison for future cohorts with chronic or acute disease. In addition to the expected difference in the microbiota of children and adults, we found that the microbiota of the elderly in this population was similar in almost all respects to that of healthy people in the same population who are scores of years younger. We speculate that this similarity is a consequence of an active healthy lifestyle and diet, although cause and effect cannot be ascribed in this (or any other) cross-sectional design. One surprising result was that the gut microbiota of persons in their 20s was distinct from those of other age cohorts, and this result was replicated, suggesting that it is a reproducible finding and distinct from those of other populations. The microbiota of the aged is variously described as being more or less diverse than that of younger cohorts, but the comparison groups used and the definitions of the aged population differ between experiments. The differences are often described by null hypothesis statistical tests, which are notoriously irreproducible when dealing with large multivariate samples. We collected and examined the gut microbiota of a cross-sectional cohort of more than 1,000 very healthy Chinese individuals who spanned ages from 3 to over 100 years. The analysis of 16S rRNA gene sequencing results used a compositional data analysis paradigm coupled with measures of effect size, where ordination, differential abundance, and correlation can be explored and analyzed in a unified and reproducible framework. Our analysis showed several surprising results compared to other cohorts. First, the overall microbiota composition of the healthy aged group was similar to that of people decades younger. Second, the major differences between groups in the gut microbiota profiles were found before age 20. Third, the gut microbiota differed little between individuals from the ages of 30 to >100. Fourth, the gut microbiota of males appeared to be more variable than that of females. Taken together, the present findings suggest that the microbiota of the healthy aged in this cross-sectional study differ little from that of the healthy young in the same population, although the minor variations that do exist depend upon the comparison cohort. IMPORTANCE We report the large-scale use of compositional data analysis to establish a baseline microbiota composition in an extremely healthy cohort of the Chinese population. This baseline will serve for comparison for future cohorts with chronic or acute disease. In addition to the expected difference in the microbiota of children and adults, we found that the microbiota of the elderly in this population was similar in almost all respects to that of healthy people in the same population who are scores of years younger. We speculate that this similarity is a consequence of an active healthy lifestyle and diet, although cause and effect cannot be ascribed in this (or any other) cross-sectional design. One surprising result was that the gut microbiota of persons in their 20s was distinct from those of other age cohorts, and this result was replicated, suggesting that it is a reproducible finding and distinct from those of other populations.

Compositional Data Analysis (CoDA) as a tool to study the (paleo)ecology of coccolithophores from coastal-neritic settings off central Portugal

2015 ◽  
Vol 319 ◽  
pp. 134-146 ◽  
Author(s):  
Catarina Guerreiro ◽  
Mário Cachão ◽  
Vera Pawlowsky-Glahn ◽  
Anabela Oliveira ◽  
Aurora Rodrigues
Keyword(s):  
Data Analysis ◽  
Compositional Data ◽  
Compositional Data Analysis ◽  
Central Portugal

scholarly journals Compositional data analysis and zeros in micro data

2000 ◽  
Vol 32 (8) ◽  
pp. 953-959 ◽  
Author(s):  
Jane M. Fry ◽  
Tim R. L. Fry ◽  
Keith R. McLaren
Keyword(s):  
Data Analysis ◽  
Compositional Data ◽  
Compositional Data Analysis ◽  
Micro Data
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