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Relationship between weedy rice (Oryza sativa) infestation level and agronomic practices in Italian rice farms

Published online by Cambridge University Press:  03 December 2020

Aldo Ferrero
Affiliation:
Full Professor, Dipartimento di Scienze Agrarie, Forestali e Alimentari, Università degli Studi di Torino, Grugliasco (TO), Italy
Silvia Fogliatto*
Affiliation:
Postdoctoral Fellow, Dipartimento di Scienze Agrarie, Forestali e Alimentari, Università degli Studi di Torino, Grugliasco (TO), Italy
Andrea Barberi
Affiliation:
Field Researcher, Innova-Tech s.r.l., Frugarolo (AL), Italy
Francesco Vidotto
Affiliation:
Associate Professor, Dipartimento di Scienze Agrarie, Forestali e Alimentari, Università degli Studi di Torino, Grugliasco (TO), Italy
*
Author for correspondence: Silvia Fogliatto, Dipartimento di Scienze Agrarie, Forestali e Alimentari, Università degli Studi di Torino, Largo P. Braccini 2, 10095 Grugliasco (TO), Italy. (E-mail: silvia.fogliatto@unito.it)
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Abstract

Weedy rice (Oryza sativa L.) is a troublesome rice (Oryza sativa L.) weed in Italy and in many other rice areas. The objective of this study was to correlate the O. sativa infestation level in northern Italy, the main European rice-growing area, with agricultural practices adopted by farmers by using data obtained from a farmer survey. In 2018 to 2019, a survey was carried out on 98 rice farms chosen to ensure different sizes, different cultivation practices, and variable degrees of O. sativa infestation. The following information was acquired: farm size; area cultivated with Clearfield® varieties; the most-adopted agronomic practices (type of tillage, crop rotation, type of sowing, water management, origin of seeds, adoption of stale seedbed, use of imazamox, presence of O. sativa resistant to imazamox); and level of O. sativa infestation: low (≤5 plants m−2), medium (>5 to 20 plants m−2), and high (>20 plants m−2). The data were analyzed through descriptive statistics and ordinal logistic regression to determine which agronomic practices influenced the level of O. sativa infestation. Farm clustering was also determined through two-step cluster analysis. Rice was cultivated as a monocrop and mainly sown in water, using purchased seeds, in plowed fields. More than half of the farms used the stale seedbed practice, and 63% adopted Clearfield® varieties, while about 45% of the farms reported imazamox-resistant O. sativa. The ordinal logistic regression underlined that use of a stale seedbed was correlated with the infestation level of O. sativa, and the two-step cluster analysis showed that the farms were mainly grouped based on the use of this technique. Most of the farms that used a stale seedbed had higher O. sativa infestation than those that did not use it, meaning that this practice was mainly applied in zones where O. sativa infestations were more serious.

Information

Type
Special Issue Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of the Weed Science Society of America
Figure 0

Figure 1. Italian rice area between Piedmont and Lombardy regions (green area) in which the survey was carried out. Yellow dots represent the farms surveyed.

Figure 1

Figure 2. Percentage of rice farms with different Oryza sativa infestation levels (low, medium, and high) on the basis of the adopted cultivation practices. (A) Total farm area and average farm area per class; (B) total farm area cultivated with Clearfield® (CL) varieties and average farm area per class; (C) tillage; (D) sowing; (E) water management; (F) seed origin; (G) crop rotation; (H) stale seedbed; (I) imazamox use; and (J) O. sativa resistance to imazamox.

Figure 2

Table 1. Agronomic practices and frequency of adopting a certain practice.

Figure 3

Table 2. Results of the ordinal logistic regression, significance of the variables, parameter estimates (log odds), odds ratios, and confidence intervals (lower and upper limits of the odds ratios).

Figure 4

Figure 3. Relative importance of each variable in the clustering, as identified by the two-step cluster analysis. Variable scoring 1 represents the most important variable in the cluster formation.

Figure 5

Figure 4. The three clusters identified in the two-step cluster analysis and the composition of each cluster for all the variables that contributed to clustering. The size of the circle and percentages close to each circle represent the proportion of farms pertaining to a certain category of each variable. The percentage of farms belonging to each cluster is reported in parentheses following the cluster name. (A) Cluster 1; (B) cluster 2; and (C) cluster 3.

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