Skip to main content Accessibility help
×
Home
Hostname: page-component-99c86f546-4k54s Total loading time: 0.221 Render date: 2021-12-04T05:37:30.035Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": true, "newCiteModal": false, "newCitedByModal": true, "newEcommerce": true, "newUsageEvents": true }

MODELLING YIELDS OF NON-IRRIGATED WINTER WHEAT IN A SEMI-ARID MEDITERRANEAN ENVIRONMENT BASED ON DROUGHT VARIABILITY

Published online by Cambridge University Press:  01 March 2013

V. G. ASCHONITIS
Affiliation:
Department of Biology and Evolution, University of Ferrara, 44121 Ferrara, Italy
A. S. LITHOURGIDIS*
Affiliation:
Department of Agronomy, Aristotle University Farm of Thessaloniki, 57001 Thermi, Greece
C. A. DAMALAS
Affiliation:
Department of Agricultural Development, Democritus University of Thrace, 68200 Orestiada, Greece
V. Z. ANTONOPOULOS
Affiliation:
Department of Hydraulics, Soil Science & Agricultural Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
*
††Corresponding author. Email: lithour@agro.auth.gr

Summary

Regression models for the prediction of grain yields of non-irrigated winter wheat in a semi-arid Mediterranean environment were developed based on drought variability. Twenty-five years (1980–2004) of climate data and yield data from four soils (sandy loam, clay, clay loam and sandy clay loam soil) in northern Greece were used for this purpose. Two variables were selected as explanatory variables of the models: (a) the monthly precipitation versus the monthly reference evapotranspiration ratio (P/ETo), which describes the monthly drought and consequently the water deficit conditions during the wheat-growing season and (b) the mean observed yield (y) of each soil, which indirectly describes the intrinsic fertility of the soils. A resampling technique using subsets of the data (bootstrapping) was applied to estimate the coefficients of the models, to assess the uncertainty of the selected explanatory variables and to validate the models. The models showed adequate predictive ability of wheat yields, defining the time and intensity of drought effects. The most crucial period for winter wheat was found to be primarily the vegetative-reproductive stage period between late winter and mid-spring (i.e. February to April). Soil clay content was found to be the most representative parameter in describing most of the physico-chemical parameters and properties of the soils and consequently the mean yield, indicating that yield is non-linearly correlated with most soil properties. With the proposed models, yield gap (YG) predictions between two growing seasons of the selected soils presented 84% accuracy in all years in the identification of the correct signal (+ or −) of yield increase or decrease, respectively, and adequate performance in the prediction of the mean YG.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Allen, R. G., Pereira, L. S., Raes, D. and Smith, M. (1998). Crop evapotranspiration – Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, FAO, Rome, Italy.Google Scholar
Austin, R. B., Morgan, C. L., Ford, M. A. and Blackwell, R. D. (1980). Contributions of grain yield from pre-anthesis assimilation in tall dwarf barley genotypes in two contrasting seasons. Annals of Botany 45:309319.CrossRefGoogle Scholar
Bakker, M. M., Govers, G., Ewert, F., Rounsevell, M. and Jones, R. (2005). Variability in regional wheat yields as a function of climate, soil and economic variables: assessing the risk of confounding. Agriculture, Ecosystems and Environment 110:195209.CrossRefGoogle Scholar
Becker-Reshef, I., Vermote, E., Lindeman, M. and Justice, C. (2010). A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sensing of Environment 114:13121323.CrossRefGoogle Scholar
Blum, A. (1998). Improving wheat grain filling under stress by stem reserve mobilisation. Euphytica 100:7783.CrossRefGoogle Scholar
Dubrovsky, M., Svoboda, M. D., Trnka, M., Hayes, M. J., Wilhite, D. A., Zalud, Z. and Hlavinka, P. (2008). Application of relative drought indices in assessing climate-change impacts on drought conditions in Czechia. Theoretical and Applied Climatology 96:155171.CrossRefGoogle Scholar
Efron, B. and Tibshirani, R. J. (1993). An Introduction to the Bootstrap. New York: Chapman & Hall.CrossRefGoogle Scholar
El Hafid, R., Smith, D. H., Karrou, M. and Samir, K. (1998). Physiological attributes associated with early-season drought resistance in spring durum wheat cultivars. Canadian Journal of Plant Science 78:227237.CrossRefGoogle Scholar
Hlavinka, P., Trnka, M., Semeradova, D., Dubrovsky, M., Zalud, Z. and Mozny, M. (2009). Effect of drought on yield variability of key crops in Czech Republic. Agricultural and Forest Meteorology 149:431442.CrossRefGoogle Scholar
Hoogenboom, G. (2000). Contribution of agrometeorology to the simulation of crop production and its applications. Agricultural and Forest Meteorology 103:137157.CrossRefGoogle Scholar
Kaufmann, R. K. and Snell, S. E. (1997). A biophysical model of corn yield: integrating climatic and social determinants. American Journal of Agricultural Economics 79:178190.CrossRefGoogle Scholar
Kimurto, P. K., Kinyua, M. G. and Njoroge, J. M. (2003). Response of bread wheat genotypes to drought simulation under a mobile rain shelter in Kenya. African Crop Science Journal 11:225234.Google Scholar
Lithourgidis, A. S., Damalas, C. A. and Gagianas, A. A. (2006). Long-term yield patterns for continuous winter wheat cropping in northern Greece. European Journal of Agronomy 25:208214.CrossRefGoogle Scholar
Lobell, D. B. and Burke, M. B. (2010). On the use of statistical models to predict crop yield responses to climate change. Agricultural and Forest Meteorology 150:14431452.CrossRefGoogle Scholar
Mavromatis, T. (2007). Drought index evaluation for assessing future wheat production in Greece. International Journal of Climatology 27:911924.CrossRefGoogle Scholar
Olesen, J. E., Bocher, P. K. and Jensen, T. (2000). Comparison of scales of climate and soil data for aggregating simulated yields of winter wheat in Denmark. Agriculture, Ecosystems and Environment 82:213228.CrossRefGoogle Scholar
Prost, L., Makowski, D. and Jeuffroy, M. H. (2008). Comparison of stepwise selection and Bayesian model averaging for yield gap analysis. Ecological Modelling 219:6676.CrossRefGoogle Scholar
Richter, G. M. and Semenov, M. A. (2005). Modelling impacts of climate change on wheat yields in England and Wales: assessing drought risks. Agricultural Systems 84:7797.CrossRefGoogle Scholar
Simane, B., Peacock, J. M. and Struik, P. C. (1993). Differences in developmental plasticity and growth rate among drought-resistant and susceptible cultivars of durum wheat (Triticum turgidum L. var. durum). Plant and Soil 157:155166.CrossRefGoogle Scholar
Stephens, D. J., Walker, G. K. and Lyons, T. J. (1994). Forecasting Australian wheat yields with a weighted rainfall index. Agricultural and Forest Meteorology 71:247263.CrossRefGoogle Scholar
Tsakiris, G. and Pangalou, D. (2009). Drought characterisation in the Mediterranean. In Coping with Drought Risk in Agriculture and Water Supply Systems. Drought Management and Policy Development in the Mediterranean, 6980 (Eds Iglesias, A., Garrote, L., Cancelliere, A., Cubillo, F. and Wilhite, D. A.). Dordrecht, Netherlands: Springer Science and Business Media B.V.CrossRefGoogle Scholar
UNEP (1992). World Atlas of Desertification. London: Edward Arnold.Google Scholar
Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. New York: Springer.CrossRefGoogle Scholar
Wassenaar, T., Lagacherie, P., Legros, J. P. and Rounsevell, M. D. A. (1999). Modelling wheat yield responses to soil and climate variability at the regional scale. Climate Research 11:209220.CrossRefGoogle Scholar
Yamoah, C. F., Walters, D. T., Shapiro, C. A., Francis, C. A. and Hayes, M. J. (2000). Standardized precipitation index and nitrogen rate effects on crop yields and risk distribution in maize. Agriculture, Ecosystems and Environment 80:113120.CrossRefGoogle Scholar
You, L., Rosegrant, M. W., Wood, S. and Sun, D. (2009). Impact of growing season temperature on wheat productivity in China. Agricultural and Forest Meteorology 149:10091014.CrossRefGoogle Scholar
5
Cited by

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

MODELLING YIELDS OF NON-IRRIGATED WINTER WHEAT IN A SEMI-ARID MEDITERRANEAN ENVIRONMENT BASED ON DROUGHT VARIABILITY
Available formats
×

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

MODELLING YIELDS OF NON-IRRIGATED WINTER WHEAT IN A SEMI-ARID MEDITERRANEAN ENVIRONMENT BASED ON DROUGHT VARIABILITY
Available formats
×

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

MODELLING YIELDS OF NON-IRRIGATED WINTER WHEAT IN A SEMI-ARID MEDITERRANEAN ENVIRONMENT BASED ON DROUGHT VARIABILITY
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *