Predicting potential winter wheat yield losses caused by multiple disease systems and climatic conditions
Само за регистроване кориснике
2017
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Yield losses in field crops are most commonly predicted by using regression models that include either biotic or abiotic factors as predictor variables. Knowing that yield loss is a complex trait, the potential capability of regression models for predicting yield losses by using models containing both biotic and abiotic factors as predictors were estimated in this study. Biotic factors considered in regression models were: leaf rust, powdery mildew, septoria tritici blotch and tan spot occurrence on the varieties Barbee and Durumko known to have various degrees of susceptibility to obligate parasites and leaf blotch diseases. Among abiotic factors, monthly averages of temperature, relative humidity and total rainfall taken from November to June for growing seasons 2006-2013 were used as predictors. In 2014, yellow rust became the predominant pathogen over leaf rust, thus 2014 and 2015 were excluded from regression models and analyzed separately. Since a high correlation was found betwe...en abiotic and biotic factors, partial least squares regression, stepwise regression and best subsets regression were applied. Best subsets regression revealed that models consisted of both biotic and abiotic factors were more precise in estimating regression coefficients and predicting future responses. The potential yield loss predictions, conducted using these models, were regressed with actual yield losses, and high coefficients of determination (R-2 = 79% for Barbee; and R-2 = 63% for Durumko) were obtained. It was also evident that using more predictors in regression models does not necessarily mean that the model would have a higher potential in making yield loss predictions. This study confirms that the relationship between a disease scoring scale and yield loss is not straightforward and higher potentials for yield loss predictions were given due to the regression models using abiotic and biotic predictor variables.
Кључне речи:
Puccinia / Blumeria / Zymoseptoria / Regression models / Yield loss / Winter wheatИзвор:
Crop Protection, 2017, 99, 17-25Издавач:
- Elsevier Sci Ltd, Oxford
Финансирање / пројекти:
- Савремено оплемењивање стрних жита за садашње и будуће потребе (RS-MESTD-Technological Development (TD or TR)-31066)
DOI: 10.1016/j.cropro.2017.05.005
ISSN: 0261-2194
WoS: 000404708700003
Scopus: 2-s2.0-85019024157
Колекције
Институција/група
FiVeRTY - JOUR AU - Jevtić, Radivoje AU - Župunski, Vesna AU - Lalošević, Mirjana AU - Župunski, Ljubica PY - 2017 UR - http://fiver.ifvcns.rs/handle/123456789/1702 AB - Yield losses in field crops are most commonly predicted by using regression models that include either biotic or abiotic factors as predictor variables. Knowing that yield loss is a complex trait, the potential capability of regression models for predicting yield losses by using models containing both biotic and abiotic factors as predictors were estimated in this study. Biotic factors considered in regression models were: leaf rust, powdery mildew, septoria tritici blotch and tan spot occurrence on the varieties Barbee and Durumko known to have various degrees of susceptibility to obligate parasites and leaf blotch diseases. Among abiotic factors, monthly averages of temperature, relative humidity and total rainfall taken from November to June for growing seasons 2006-2013 were used as predictors. In 2014, yellow rust became the predominant pathogen over leaf rust, thus 2014 and 2015 were excluded from regression models and analyzed separately. Since a high correlation was found between abiotic and biotic factors, partial least squares regression, stepwise regression and best subsets regression were applied. Best subsets regression revealed that models consisted of both biotic and abiotic factors were more precise in estimating regression coefficients and predicting future responses. The potential yield loss predictions, conducted using these models, were regressed with actual yield losses, and high coefficients of determination (R-2 = 79% for Barbee; and R-2 = 63% for Durumko) were obtained. It was also evident that using more predictors in regression models does not necessarily mean that the model would have a higher potential in making yield loss predictions. This study confirms that the relationship between a disease scoring scale and yield loss is not straightforward and higher potentials for yield loss predictions were given due to the regression models using abiotic and biotic predictor variables. PB - Elsevier Sci Ltd, Oxford T2 - Crop Protection T1 - Predicting potential winter wheat yield losses caused by multiple disease systems and climatic conditions EP - 25 SP - 17 VL - 99 DO - 10.1016/j.cropro.2017.05.005 ER -
@article{ author = "Jevtić, Radivoje and Župunski, Vesna and Lalošević, Mirjana and Župunski, Ljubica", year = "2017", abstract = "Yield losses in field crops are most commonly predicted by using regression models that include either biotic or abiotic factors as predictor variables. Knowing that yield loss is a complex trait, the potential capability of regression models for predicting yield losses by using models containing both biotic and abiotic factors as predictors were estimated in this study. Biotic factors considered in regression models were: leaf rust, powdery mildew, septoria tritici blotch and tan spot occurrence on the varieties Barbee and Durumko known to have various degrees of susceptibility to obligate parasites and leaf blotch diseases. Among abiotic factors, monthly averages of temperature, relative humidity and total rainfall taken from November to June for growing seasons 2006-2013 were used as predictors. In 2014, yellow rust became the predominant pathogen over leaf rust, thus 2014 and 2015 were excluded from regression models and analyzed separately. Since a high correlation was found between abiotic and biotic factors, partial least squares regression, stepwise regression and best subsets regression were applied. Best subsets regression revealed that models consisted of both biotic and abiotic factors were more precise in estimating regression coefficients and predicting future responses. The potential yield loss predictions, conducted using these models, were regressed with actual yield losses, and high coefficients of determination (R-2 = 79% for Barbee; and R-2 = 63% for Durumko) were obtained. It was also evident that using more predictors in regression models does not necessarily mean that the model would have a higher potential in making yield loss predictions. This study confirms that the relationship between a disease scoring scale and yield loss is not straightforward and higher potentials for yield loss predictions were given due to the regression models using abiotic and biotic predictor variables.", publisher = "Elsevier Sci Ltd, Oxford", journal = "Crop Protection", title = "Predicting potential winter wheat yield losses caused by multiple disease systems and climatic conditions", pages = "25-17", volume = "99", doi = "10.1016/j.cropro.2017.05.005" }
Jevtić, R., Župunski, V., Lalošević, M.,& Župunski, L.. (2017). Predicting potential winter wheat yield losses caused by multiple disease systems and climatic conditions. in Crop Protection Elsevier Sci Ltd, Oxford., 99, 17-25. https://doi.org/10.1016/j.cropro.2017.05.005
Jevtić R, Župunski V, Lalošević M, Župunski L. Predicting potential winter wheat yield losses caused by multiple disease systems and climatic conditions. in Crop Protection. 2017;99:17-25. doi:10.1016/j.cropro.2017.05.005 .
Jevtić, Radivoje, Župunski, Vesna, Lalošević, Mirjana, Župunski, Ljubica, "Predicting potential winter wheat yield losses caused by multiple disease systems and climatic conditions" in Crop Protection, 99 (2017):17-25, https://doi.org/10.1016/j.cropro.2017.05.005 . .