An index of crop water stress related to wheat and grain sorghum yields

Nix, H. A.; Fitzpatrick, E. A.

doi:10.1016/0002-1571(69)90024-7

Cited by 109 publications

(28 citation statements)

References 19 publications

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“…Most studies reported that grain yield increases tend to be associated more often with post-anthesis water use rather than before-anthesis or total water use (Anderson, 1992). The observation of yield increase with pre-anthesis water tends to occur in environments where the rain falls mainly in summer and crops grow largely on stored water (Nix and Fitzpatrick, 1969) to conserve water for use during the grain filling period.…”

Section: Discussionmentioning

confidence: 99%

Crop yield and water use efficiency in semi-arid region of Turkey

Çayçı

Heng

Öztürk

et al. 2009

Soil and Tillage Research

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Section: Discussionmentioning

confidence: 99%

Crop yield and water use efficiency in semi-arid region of Turkey

Çayçı

Heng

Öztürk

et al. 2009

Soil and Tillage Research

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“…Simulation modelling of the wheat crop has advanced greatly since the early efforts of Nix and Fitzpatrick (1969), in line with increased physiological understanding, more transparent model structures, and much more powerful computing capacity. A comparison by Jamieson et al (1998) of five wheat models against carefully measured wheat crops under a wide range of water treatments in New Zealand (yields from 3.5 to 9.9 t/ha) showed that the AFRCWHEAT2 model from the UK to be best for yield prediction [root mean squared deviation (RMSD) = 0.64 t/ha, c.f.…”

Section: Simulation Modellingmentioning

confidence: 99%

Wheat physiology: a review of recent developments

Fischer¹

2011

Crop Pasture Sci.

339

251

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Abstract. This review focuses on recent advances in some key areas of wheat physiology, namely phasic development, determination of potential yield and water-limited potential yield, tolerance to some other abiotic stresses (aluminium, salt, heat shock), and simulation modelling. Applications of the new knowledge to breeding and crop agronomy are emphasized. The linking of relatively simple traits like time to flowering, and aluminium and salt tolerance, in each case to a small number of genes, is being greatly facilitated by the development of molecular gene markers, and there is some progress on the functional basis of these links, and likely application in breeding. However with more complex crop features like potential yield, progress at the gene level is negligible, and even that at the level of the physiology of seemingly important component traits (e.g., grain number, grain weight, soil water extraction, sensitivity to water shortage at meiosis) is patchy and generally slow although a few more heritable traits (e.g. carbon isotope discrimination, coleoptile length) are seeing application. This is despite the advent of smart tools for molecular analysis and for phenotyping, and the move to study genetic variation in soundly-constituted populations. Exploring the functional genomics of traits has a poor record of application; while trait validation in breeding appears underinvested. Simulation modeling is helping to unravel G Â E interaction for yield, and is beginning to explore genetic variation in traits in this context, but adequate validation is often lacking. Simulation modelling to project agronomic options over time is, however, more successful, and has become an essential tool, probably because less uncertainty surrounds the influence of variable water and climate on the performance of a given cultivar. It is the everincreasing complexity we are seeing with genetic variation which remains the greatest challenge for modelling, molecular biology, and indeed physiology, as they all seek to progress yield at a rate greater than empirical breeding is achieving.

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“…This stress index (SI) was used in a simple regression model to predict wheat yield for each wheat-producing shire in Australia. The index was similar in concept to that proposed by Nix and Fitzpatrick (1969). It utilizes biophysical knowledge of the crop, allows consideration of soil type effects, and derives the SI by contrasting soil water supply with crop demand.…”

Section: Case Study 1: Wheatmentioning

confidence: 99%

On measuring quality of a probabilistic commodity forecast for a system that incorporates seasonal climate forecasts

Potgieter¹,

Everingham

Hammer

2003

Intl Journal of Climatology

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Regional commodity forecasts are being used increasingly in agricultural industries to enhance their risk management and decision-making processes. These commodity forecasts are probabilistic in nature and are often integrated with a seasonal climate forecast system. The climate forecast system is based on a subset of analogue years drawn from the full climatological distribution. In this study we sought to measure forecast quality for such an integrated system. We investigated the quality of a commodity (i.e. wheat and sugar) forecast based on a subset of analogue years in relation to a standard reference forecast based on the full climatological set. We derived three key dimensions of forecast quality for such probabilistic forecasts: reliability, distribution shift, and change in dispersion. A measure of reliability was required to ensure no bias in the forecast distribution. This was assessed via the slope of the reliability plot, which was derived from examination of probability levels of forecasts and associated frequencies of realizations. The other two dimensions related to changes in features of the forecast distribution relative to the reference distribution. The relationship of 13 published accuracy/skill measures to these dimensions of forecast quality was assessed using principal component analysis in case studies of commodity forecasting using seasonal climate forecasting for the wheat and sugar industries in Australia. There were two orthogonal dimensions of forecast quality: one associated with distribution shift relative to the reference distribution and the other associated with relative distribution dispersion. Although the conventional quality measures aligned with these dimensions, none measured both adequately. We conclude that a multi-dimensional approach to assessment of forecast quality is required and that simple measures of reliability, distribution shift, and change in dispersion provide a means for such assessment. The analysis presented was also relevant to measuring quality of probabilistic seasonal climate forecasting systems. The importance of retaining a focus on the probabilistic nature of the forecast and avoiding simplifying, but erroneous, distortions was discussed in relation to applying this new forecast quality assessment paradigm to seasonal climate forecasts.

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An index of crop water stress related to wheat and grain sorghum yields

Cited by 109 publications

References 19 publications

Crop yield and water use efficiency in semi-arid region of Turkey

Crop yield and water use efficiency in semi-arid region of Turkey

Wheat physiology: a review of recent developments

On measuring quality of a probabilistic commodity forecast for a system that incorporates seasonal climate forecasts

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