Effect of Soil pH on the Growth, Reproductive Investment and Pollen Allergenicity of Ambrosia artemisiifolia L.

Gentili, Rodolfo; Ambrosini, Roberto; Montagnani, Chiara; Citterio, Sandra

doi:10.3389/fpls.2018.01335

Cited by 163 publications

(104 citation statements)

References 64 publications

(72 reference statements)

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“…This is also supported by the positive correlation between crop cover and application of P (as well as N) fertilizer in our dataset (Electronic Appendix 1). Although several relationships between certain soil properties and the abundance of ragweed are reported in previous studies (Pinke et al 2011(Pinke et al , 2013(Pinke et al , 2016(Pinke et al , 2018 and similar correlations reported by other authors (Hui-na et al 2014, Onen et al 2017, Gentili et al 2018, the importance of P reported here is a new finding. In our dataset the P content of the soil was not correlated with the other factors.…”

Section: General Predictors For the Whole Area Studiedsupporting

confidence: 90%

Drivers of Ambrosia artemisiifolia abundance in arable fields along the Austrian-Hungarian border

Pinke¹,

Kolejanisz²,

Vér³

et al. 2019

Preslia

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The Carpathian Basin is one of the most important regions in terms of the invasion of the common ragweed (Ambrosia artemisiifolia) in Europe. The invasion history of this weed, however, seems to have been assessed differently in Austria and Hungary: scientists in both countries assumed that this species had become abundant earlier and had caused more problems in their own than in other country. The goal of this study is to resolve the historical misunderstandings and scrutinize the related popular beliefs by a concise literature overview and an extensive analysis of the current patterns in ragweed infestations in crops in the borderlands in eastern Austria and western Hungary. The abundance of A. artemisiifolia was measured in 200 arable fields across the region, along with 31 background variables. Data were analysed using binomial generalized linear models (GLM), decision tree models and variation partitioning. Ambrosia artemisiifolia occurred more frequently in Hungary, but there were no significant differences in the proportion of larger cover values recorded in these two countries, and 'cover values > 10%' were even slightly more common in Austria. We found that previous crops of maize and soya bean and conventional farming were associated with the higher abundances in Austria, while organic farming was associated with relatively higher frequencies of heavy infestations in Hungarian fields. In the overall analysis crop cover was the most important variable with low crop cover associated with high ragweed abundance. Temperature and phosphorous fertilizer were negatively, while precipitation and soil phosphorous concentration positively associated with the abundance values. Land-use variables accounted for more of the variance in the abundance patterns of common ragweed than environmental variables. The current patterns in ragweed distribution might indicate that a saturation process is still underway on the Austrian side. The saturation lag of 20-30 years is possibly due to several factors and the role of the Iron Curtain in determining cross-border exchange of propagules could be decisive. Nevertheless, the discrepancies uncovered in the accounts of the invasion of Hungarian and Austrian authors might also be seen as legacies of the Iron Curtain, which were caused by mutual limitations on access to national data and literature of the other country in a critical period of rapid ragweed spread. These discrepancies, that had a long-lasting effect on the work of scientific communities, are documented here in detail for the first time. K e y w o r d s: agriculture, arable fields, common ragweed, invasion, invasive plants, ragweed, spread, weed distribution, weed ecology

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Section: General Predictors For the Whole Area Studiedsupporting

confidence: 90%

Drivers of Ambrosia artemisiifolia abundance in arable fields along the Austrian-Hungarian border

Pinke¹,

Kolejanisz²,

Vér³

et al. 2019

Preslia

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“…It greatly influences the behavior of chemical elements and, along with organic matter (OM), is the most important parameter determining trace metal partitioning and aqueous speciation in soils [18]. It is considered of special importance in connection with nutrients [19] and micronutrients [20], and it is the key variable that affects plant growth [21]. It controls soil fertility, regulates soil biogeochemical processes, and influences the structure and functioning of terrestrial ecosystems [22].…”

Section: Introductionmentioning

confidence: 99%

Comparing Machine Learning Models and Hybrid Geostatistical Methods Using Environmental and Soil Covariates for Soil pH Prediction

Tziachris¹,

Aschonitis²,

Chatzistathis³

et al. 2020

IJGI

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In the current paper we assess different machine learning (ML) models and hybrid geostatistical methods in the prediction of soil pH using digital elevation model derivates (environmental covariates) and co-located soil parameters (soil covariates). The study was located in the area of Grevena, Greece, where 266 disturbed soil samples were collected from randomly selected locations and analyzed in the laboratory of the Soil and Water Resources Institute. The different models that were assessed were random forests (RF), random forests kriging (RFK), gradient boosting (GB), gradient boosting kriging (GBK), neural networks (NN), and neural networks kriging (NNK) and finally, multiple linear regression (MLR), ordinary kriging (OK), and regression kriging (RK) that although they are not ML models, they were used for comparison reasons. Both the GB and RF models presented the best results in the study, with NN a close second. The introduction of OK to the ML models’ residuals did not have a major impact. Classical geostatistical or hybrid geostatistical methods without ML (OK, MLR, and RK) exhibited worse prediction accuracy compared to the models that included ML. Furthermore, different implementations (methods and packages) of the same ML models were also assessed. Regarding RF and GB, the different implementations that were applied (ranger-ranger, randomForest-rf, xgboost-xgbTree, xgboost-xgbDART) led to similar results, whereas in NN, the differences between the implementations used (nnet-nnet and nnet-avNNet) were more distinct. Finally, ML models tuned through a random search optimization method were compared with the same ML models with their default values. The results showed that the predictions were improved by the optimization process only where the ML algorithms demanded a large number of hyperparameters that needed tuning and there was a significant difference between the default values and the optimized ones, like in the case of GB and NN, but not in RF. In general, the current study concluded that although RF and GB presented approximately the same prediction accuracy, RF had more consistent results, regardless of different packages, different hyperparameter selection methods, or even the inclusion of OK in the ML models’ residuals.

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“…In addition to the interference in root growth, pH can change the nutrient absorption capacity, which may lead to mineral deficiency or toxicity by metals such as aluminum (Brown, Koenig, Huggins, Harsh, & Rossi, 2008; Gentili, Ambrosini, Montagnani, Caronni, & Citterio, 2018). Calcium may favor cell elongation and expansion, increasing root growth (Fageria & Moreira, 2011).…”

Section: Introductionmentioning

confidence: 99%

Root growth and crop performance of soybean under chemical, physical, and biological changes after subsoiling

et al. 2020

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Chemical, physical and biological soil attributes can facilitate soybean root growth in greater volume and depth in the soil, which can minimize yield reduction caused by water deficit. Soil management can contribute positively or negatively to these soil attributes. The aim of this work was to evaluate the root growth and crop performance of soybean, in response to chemical, physical and biological changes after subsoiling at different depths. At the R5 phenological stage, trenches were made for sampling and soil collection for chemical, physical and biological analysis and root growth was carried out. At V5, V7, R2 and R5 stages, plants were collected to evaluate height, leaf area and dry mass. At V5, stage number and dry mass of the nodules were evaluated. Subsoiling increased pH and Ca, and decreased Al in the soil, resulted in higher relative density and did not affect in mechanical penetration resistance compared to non-subsoiled soil. Basal respiration and soybean nodulation were higher in the subsoiled soil. Up to 15 cm depth, there were 87.91% of the total root dry mass and 78.79% of the total root volume. Initial and final plant growth were the same in subsoiled and non-subsoiled soil. Number of nodules in the subsoiled soil was 28% higher than in the non-subsoiled soil. Under these study conditions, subsoiling provides lower root growth but benefits grain yield.

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Effect of Soil pH on the Growth, Reproductive Investment and Pollen Allergenicity of Ambrosia artemisiifolia L.

Cited by 163 publications

References 64 publications

Drivers of Ambrosia artemisiifolia abundance in arable fields along the Austrian-Hungarian border

Drivers of Ambrosia artemisiifolia abundance in arable fields along the Austrian-Hungarian border

Comparing Machine Learning Models and Hybrid Geostatistical Methods Using Environmental and Soil Covariates for Soil pH Prediction

Root growth and crop performance of soybean under chemical, physical, and biological changes after subsoiling

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