Modern bread wheat (Triticum aestivum) has been well adapted for survival and production in water-limited environments since it was first domesticated in the Mediterranean basin at least 8000 years ago. Adaptation to various environments has been assisted through selection and cross-breeding for traits that contribute to high and stable yield since that time. Improvements in crop management aimed at improving yield and grain quality probably developed more slowly but the rate of change has accelerated in recent decades. Many studies have shown that the contribution to increased yield from improved management has been about double that from breeding. Both processes have proceeded in parallel, although possibly at different rates in some periods, and positive interactions between breeding and management have been responsible for greater improvements than by either process alone. In southern Australia, management of the wheat crop has focused on improvement of yield and grain quality over the last century. Adaptation has come to be equated with profitability and, recently, with long-term economic and biological viability of the production system. Early emphases on water conservation through the use of bare fallow, crop nutrition through the use of fertilisers, crop rotation with legumes, and mechanisation, have been replaced by, or supplemented with, extensive use of herbicides for weed management, reduced tillage, earlier sowing, retention of crop residues, and the use of ‘break’ crops, largely for management of root diseases. Yields from rainfed wheat crops in Western Australia have doubled since the late 1980s and water-use efficiency has also doubled. The percentage of the crop in Western Australia that qualifies for premium payments for quality has increased 3–4 fold since 1990. Both these trends have been underpinned by the gradual elimination or management of the factors that have been identified as limiting grain yield, grain quality, or long-term viability of the cropping system.

In this paper we analyse existing experimental data (grain yield and yield components) from seed rate experiments on wheat in Western Australia, with the aims of determining which factors most influence the optimum plant population, and advancing some practical guidelines for improving the choice of seed rate under rain-fed conditions. Experiments (32) were conducted in the rain-fed cropping zone of Western Australia between 1996 and 2001, using factorial combinations of wheat cultivars (3–25) and target plant populations (4 or 5). Some of them also contained treatments of nitrogen fertiliser (0 or 40 kg/ha of N) or sowing times (2). Each cultivar × plant population dataset (248) was considered to be a record for the sake of the subsequent analyses. Actual plant numbers were counted in each experiment and the optimum plant population was estimated when the slope of an inverse polynomial curve (choosing the most appropriate of the LDL and QDL models in GenStat) fitted to each record was 2.5 kg/ha of grain yield for each extra plant/m2. The optimum populations were initially grouped using a regression tree technique into groups with similar characteristics using pre-sowing rainfall, rainfall in the growing season, sowing date, and soil type. The variables cultivar and nitrogen fertiliser rate were later added to the regression tree analysis. Yield components available for most experiments were used as an aid to interpretation of the results. The optimum plant population varied from 35 to 175 plants/m2 and average grain yields varied from 0.42 to 3.91 t/ha. Rainfall in the growing season (sowing date to harvest date) provided the first split in the regression tree, but pre-sowing rainfall (January to sowing date), sowing date, and soil type further modified the optimum population. The addition of N fertiliser rate as a variable in the regression tree did not induce any different groupings of the optimum population sets, but cultivars were grouped into 4 response types according to pre- and post-sowing rainfall amounts. Where rainfall in the growing season was <205 mm, improved growing conditions due to more pre-sowing rainfall, earlier sowing, and more seasonal rainfall, were associated with higher optimum plant populations. Where rainfall in the growing season exceeded 205 mm, higher pre-sowing rainfall was associated with lower optimum populations. The optimum population was greater on sands than on clay loams. However, on sandy loam soils the optimum was less where rainfall in the growing season was 291 mm. Increases in yield components in response to improved growing conditions above about 400 culms/m2, 300 ears/m2, 10 000 kernels/m2, and 600 g/m2 of dry matter at anthesis were not associated with higher optimum plant populations. In general, the optimum plant population increased at about 40 plants/m2 for each tonne of grain yield up to about 3.0 t/ha. The effect of cultivar on the optimum population appeared at yield levels above 2.5 t/ha, but was only detectable when the rainfall in the growing season exceeded 205 mm. Growing conditions and cultivars associated with lower weight per ear (due to fewer kernels and/or lower kernel weight) were associated with higher optimum plant population when the rainfall in the growing season exceeded 205 mm. It is suggested that farmers can make better estimates of the appropriate plant population (and hence can calculate seed rate) on the basis of pre-sowing rainfall (likely stored water), rainfall zone (probability of rainfall in the growing season), sowing date, soil type, and characteristics of individual cultivars where known.

Sowing wheat (Triticum aestivum L.) at the right time is one of the most important means of maximising grain yield in dryland agriculture. Objectives of this study were to understand the variation in estimates of optimum sowing time as influenced by cultivar and environmental characteristics, and to assess the relative importance of location, season, sowing time, and cultivar factors in maximising grain yield in Western Australia. Twenty-seven cultivar × time of sowing experiments were conducted over three seasons (2003–05) at a range of locations (annual rainfall 300–450 mm, lat. 28–35°S). There were four types of cultivar × sowing time responses, namely, quadratic, linear declining, flat, and linear increasing, associated with opening rains before mid-May, opening rains after mid-May, low-yielding sites, and good spring rains, respectively. Regression-tree analysis revealed that differences among cultivars in Tmax (sowing time when maximum grain yield was achieved) were much less in the eastern sites (mostly drier seasons). A biplot differentiated cultivars for Tmax across the range of environments used, while the subset regression analysis specifically indicated an association of average temperature and growing-season rainfall with variation for Tmax of individual cultivars. The yield penalty for sowing before the optimum time in quadratic-type responses was clearly greater for shorter season cultivars but no clear relationship was apparent between maturity class of cultivars and the penalty for late sowing, possibly due to differential plasticity of cultivars for grain weight under harsh finishing conditions. The duration of the optimum sowing window at a given location was inversely proportional to the yield potential, implying that it is critical to sow at or close to the optimum time when the yield potential is high, most common when the season breaks early. Yield component analysis showed that the relative change in grain yield over sowing dates was significantly correlated with relative changes in grain numbers/m2 in the late May sowings but other yield components were also important in the early May experiments. Sowing time accounted for 10% of grain yield variation compared with cultivar (1%), while the rest was due to uncontrollable factors of location and season.

Temperatures below or above optimal growth conditions are among the major stressors affecting productivity, end‐use quality, and distribution of key staple crops including rice ( Oryza sativa ), wheat ( Triticum aestivum ), and maize ( Zea mays L.). Among temperature stresses, cold stress induces cellular changes that cause oxidative stress and slowdown metabolism, limit growth, and ultimately reduce crop productivity. Perception of cold stress by plant cells leads to the activation of cold‐responsive transcription factors and downstream genes, which ultimately impart cold tolerance. The response triggered in crops to cold stress includes gene expression/suppression, the accumulation of sugars upon chilling, and signaling molecules, among others. Much of the information on the effects of cold stress on perception, signal transduction, gene expression, and plant metabolism are available in the model plant Arabidopsis but somewhat lacking in major crops. Hence, a complete understanding of the molecular mechanisms by which staple crops respond to cold stress remain largely unknown. Here, we make an effort to elaborate on the molecular mechanisms employed in response to low‐temperature stress. We summarize the effects of cold stress on the growth and development of these crops, the mechanism of cold perception, and the role of various sensors and transducers in cold signaling. We discuss the progress in cold tolerance research at the genome, transcriptome, proteome, and metabolome levels and highlight how these findings provide opportunities for designing cold‐tolerant crops for the future.