Potential abiotic stress targets for modern genetic manipulation.

Research into crop yield and resilience has underpinned global food security, evident in yields tripling in the past 5 decades. The challenges that global agriculture now faces are not just to feed 10+ billion people within a generation, but to do so under a harsher, more variable, and less predictable climate, and in many cases with less water, more expensive inputs, and declining soil quality. The challenges of climate change are not simply to breed for a "hotter drier climate," but to enable resilience to floods and droughts and frosts and heat waves, possibly even within a single growing season. How well we prepare for the coming decades of climate variability will depend on our ability to modify current practices, innovate with novel breeding methods, and communicate and work with farming communities to ensure viability and profitability. Here we define how future climates will impact farming systems and growing seasons, thereby identifying the traits and practices needed and including exemplars being implemented and developed. Critically, this review will also consider societal perspectives and public engagement about emerging technologies for climate resilience, with participatory approaches presented as the best approach.

Seedling and field assessment of wheat (Triticum aestivum L.) dwarfing genes and their influence on root traits in multiple genetic backgrounds.

Deployment of the Rht-B1b and Rht-D1b dwarfing genes helped facilitate the Green Revolution to increase wheat yields globally. Much is known of the influence of these genes on plant height and agronomic performance, but not of their effects on root architecture. We assessed 29 near-isogenic lines (NILs) representing 11 Green Revolution and alternative dwarfing genes across multiple genetic backgrounds for root architecture characteristics in controlled and field environments. Genetic background did not influence plant height, but had a small and significant (P<0.05) effect on root architecture. All dwarfing gene NILs were significantly (P<0.01) shorter compared with tall controls. The Green Revolution Rht-B1b and Rht-D1b sometimes had longer seedling roots but were not different from their respective tall controls for root depth in the field. The Rht8, Rht12, and Rht18 dwarfing gene NILs produced long seminal roots in seedling pouches, and a greater maximum rooting depth (MRD) and root penetration rate (RPR) in the field. Genotypic increases in MRD and RPR were strongly correlated with increased harvest index and grain yield, particularly in dry environments. Careful root phenotyping highlights the potential of novel dwarfing genes for wheat genetic improvement under water-limited conditions.

Selection for early shoot vigour in wheat increases root hair length but reduces epidermal cell size of roots and leaves.

Six cycles of recurrent selection for early shoot vigour in wheat resulted in significant increases in leaf width and shoot biomass. Here, in replicated controlled-environment studies, the effect of early shoot vigour on root biomass, rhizosheath size, root hair length, and cell size in the roots and leaves was examined across different cycles of selection. Increased shoot vigour was associated with greater root biomass, larger rhizosheath size, and longer root hairs. Our findings demonstrate that rhizosheath size was a reliable surrogate for root hair length in this germplasm. Examination of the root epidermis revealed that the 'cell body' of the trichoblasts (hair-forming cells) and the atrichoblasts (non-hair-forming cells) decreased in size as shoot vigour increased. Therefore, in higher vigour germplasm, longer root hairs emerged from smaller trichoblasts so that total trichoblast volume (root hair plus cell body) was generally similar regardless of shoot vigour. Similarly, the sizes of the four main cell types on the leaf epidermis became progressively smaller as shoot vigour increased, which also increased stomatal density. The relationship between shoot vigour and root traits is considered, and the potential contribution of below-ground root traits to performance and competitiveness of high vigour germplasm is discussed.

Impact of Varying Light and Dew on Ground Cover Estimates from Active NDVI, RGB, and LiDAR.

Canopy ground cover (GC) is an important agronomic measure for evaluating crop establishment and early growth. This study evaluates the reliability of GC estimates, in the presence of varying light and dew on leaves, from three different ground-based sensors: (1) normalized difference vegetation index (NDVI) from the commercially available GreenSeeker®; (2) RGB images from a digital camera, where GC was determined as the portion of pixels from each image meeting a greenness criterion (i.e., (Green - Red)/(Green + Red) > 0); and (3) LiDAR using two separate approaches: (a) GC from LiDAR red reflectance (whereby red reflectance less than five was classified as vegetation) and (b) GC from LiDAR height (whereby height greater than 10 cm was classified as vegetation). Hourly measurements were made early in the season at two different growth stages (tillering and stem elongation), among wheat genotypes highly diverse for canopy characteristics. The active NDVI showed the least variation through time and was particularly stable, regardless of the available light or the presence of dew. In addition, between-sample-time Pearson correlations for NDVI were consistently high and significant (P < 0.0001), ranging from 0.89 to 0.98. In comparison, GC from LiDAR and RGB showed greater variation across sampling times, and LiDAR red reflectance was strongly influenced by the presence of dew. Excluding times when the light was exceedingly low, correlations between GC from RGB and NDVI were consistently high (ranging from 0.79 to 0.92). The high reliability of the active NDVI sensor potentially affords a high degree of flexibility for users by enabling sampling across a broad range of acceptable light conditions.

Ground-Based LiDAR Improves Phenotypic Repeatability of Above-Ground Biomass and Crop Growth Rate in Wheat.

Highly repeatable, nondestructive, and high-throughput measures of above-ground biomass (AGB) and crop growth rate (CGR) are important for wheat improvement programs. This study evaluates the repeatability of destructive AGB and CGR measurements in comparison to two previously described methods for the estimation of AGB from LiDAR: 3D voxel index (3DVI) and 3D profile index (3DPI). Across three field experiments, contrasting in available water supply and comprising up to 98 wheat genotypes varying for canopy architecture, several concurrent measurements of LiDAR and AGB were made from jointing to anthesis. Phenotypic correlations at discrete events between AGB and the LiDAR-derived biomass indices were significant, ranging from 0.31 (P < 0.05) to 0.86 (P < 0.0001), providing confidence in the LiDAR indices as effective surrogates for AGB. The repeatability of the LiDAR biomass indices at discrete events was at least similar to and often higher than AGB, particularly under water limitation. The correlations between calculated CGR for AGB and the LiDAR indices were moderate to high and varied between experiments. However, across all experiments, the repeatabilities of the CGR derived from the LiDAR indices were appreciably greater than those for AGB, except for the 3DPI in the water-limited environment. In our experiments, the repeatability of either LiDAR index was consistently higher than that of AGB, both at discrete time points and when CGR was calculated. These findings provide promising support for the reliable use of ground-based LiDAR, as a surrogate measure of AGB and CGR, for screening germplasm in research and wheat breeding.

A reduced-tillering trait shows small but important yield gains in dryland wheat production.

Reducing the number of tillers per plant using a tiller inhibition (tin) gene has been considered as an important trait for wheat production in dryland environments. We used a spatial analysis approach with a daily time-step coupled radiation and transpiration efficiency model to simulate the impact of the reduced-tillering trait on wheat yield under different climate change scenarios across Australia's arable land. Our results show a small but consistent yield advantage of the reduced-tillering trait in the most water-limited environments both under current and likely future conditions. Our climate scenarios show that whilst elevated [CO2 ] (e[CO2 ]) alone might limit the area where the reduced-tillering trait is advantageous, the most likely climate scenario of e[CO2 ] combined with increased temperature and reduced rainfall consistently increased the area where restricted tillering has an advantage. Whilst long-term average yield advantages were small (ranged from 31 to 51 kg ha-1  year-1 ), across large dryland areas the value is large (potential cost-benefits ranged from Australian dollar 23 to 60 MIL/year). It seems therefore worthwhile to further explore this reduced-tillering trait in relation to a range of different environments and climates, because its benefits are likely to grow in future dry environments where wheat is grown around the world.

Evaluation of the Phenotypic Repeatability of Canopy Temperature in Wheat Using Continuous-Terrestrial and Airborne Measurements.

Infrared canopy temperature (CT) is a well-established surrogate measure of stomatal conductance. There is ample evidence showing that genotypic variation in stomatal conductance is associated with grain yield in wheat. Our goal was to determine when CT repeatability is greatest (throughout the season and within the day) to guide CT deployment for research and wheat breeding. CT was measured continuously with ArduCrop wireless infrared thermometers from post-tillering to physiological maturity, and with airborne thermography on cloudless days from manned helicopter at multiple times before and after flowering. Our experiments in wheat, across two years contrasting for water availability, showed that repeatability for CT was greatest later in the season, during grain-filling, and usually in the afternoon. This was supported by the observation that repeatability for ArduCrop, and more so for airborne CT, was significantly associated (P < 0.0001) with calculated clear-sky solar radiation and to a lesser degree, vapor pressure deficit. Adding vapor pressure deficit to a model comprising either clear-sky solar radiation or its determinants, day-of-year and hour-of-day, made little to no improvement to the coefficient of determination. Phenotypic correlations for airborne CT afternoon sampling events were consistently high between events in the same year, more so for the year when soil water was plentiful (r = 0.7 to 0.9) than the year where soil water was limiting (r = 0.4 to 0.9). Phenotypic correlations for afternoon airborne CT were moderate between years contrasting in soil water availability (r = 0.1 to 0.5) and notably greater on two separate days following irrigation or rain in the drier year, ranging from r = 0.39 to 0.53 (P < 0.0001) for the midday events. For ArduCrop CT the pattern of phenotypic correlations, within a given year, was similar for both years: phenotypic correlations were higher during the grain-filling months of October and November and for hours-of-day from 11 onwards. The lowest correlations comprised events from hours-of-day 8 and 9 across all months. The capacity for the airborne method to instantaneously sample CT on hundreds of plots is more suited to large field experiments than the static ArduCrop sensors which measure CT continuously on a single experimental plot at any given time. Our findings provide promising support for the reliable deployment of CT phenotyping for research and wheat breeding, whereby the high repeatability and high phenotypic correlations between afternoon sampling events during grain-filling could enable reliable screening of germplasm from only one or two sampling events.

Wheat drought tolerance in the field is predicted by amino acid responses to glasshouse-imposed drought.

Water limits crop productivity, so selecting for a minimal yield gap in drier environments is critical to mitigate against climate change and land-use pressure. We investigated the responses of relative water content (RWC), stomatal conductance, chlorophyll content, and metabolites in flag leaves of commercial wheat (Triticum aestivum L.) cultivars to three drought treatments in the glasshouse and in field environments. We observed strong genetic associations between glasshouse-based RWC, metabolites, and yield gap-based drought tolerance (YDT; the ratio of yield in water-limited versus well-watered conditions) across 18 field environments spanning sites and seasons. Critically, RWC response to glasshouse drought was strongly associated with both YDT (r2=0.85, P<8E-6) and RWC under field drought (r2=0.77, P<0.05). Moreover, multiple regression analyses revealed that 98% of genetic YDT variance was explained by drought responses of four metabolites: serine, asparagine, methionine, and lysine (R2=0.98; P<0.01). Fitted coefficients suggested that, for given levels of serine and asparagine, stronger methionine and lysine accumulation was associated with higher YDT. Collectively, our results demonstrate that high-throughput, targeted metabolic phenotyping of glasshouse-grown plants may be an effective tool for selection of wheat cultivars with high field-derived YDT.

Deeper roots associated with cooler canopies, higher normalized difference vegetation index, and greater yield in three wheat populations grown on stored soil water.

Simple and repeatable methods are needed to select for deep roots under field conditions. A large-scale field experiment was conducted to assess the association between canopy temperature (CT) measured by airborne thermography and rooting depth determined by the core-break method. Three wheat populations, C306×Westonia (CW), Hartog×Drysdale (HD), and Sundor×Songlen (SS), were grown on stored soil water in NSW Australia in 2017 (n=196-252). Cool and warm CT extremes ('tails') were cored after harvest (13-32% of each population). Rooting depth was significantly correlated with CT at late flowering (r= -0.25, -0.52, and -0.23 for CW, HD, and SS, respectively, P<0.05 hereafter), with normalized difference vegetation index (NDVI) at early grain filling (r=0.30-0.39), and with canopy height (r=0.23-0.48). The cool tails showed significantly deeper roots than the respective warm tails by 8.1 cm and 6.2 cm in CW and HD, and correspondingly, greater yields by an average 19% and 7%, respectively. This study highlighted that CT measured rapidly by airborne thermography or NDVI at early grain filling could be used to guide selection of lines with deeper roots to increase wheat yields. The remote measurement methods in this study were repeatable and high throughput, making them well suited to use in breeding programmes.

Modelling impact of early vigour on wheat yield in dryland regions.

Early vigour, or faster early leaf area development, has been considered an important trait for rainfed wheat in dryland regions such as Australia. However, early vigour is a genetically complex trait, and results from field experiments have been highly variable. Whether early vigour can lead to improved water use efficiency and crop yields is strongly dependent on climate and management conditions across the entire growing season. Here, we present a modelling framework for simulating the impact of early vigour on wheat growth and yield at eight sites representing the major climate types in Australia. On a typical soil with plant available water capacity (PAWC) of 147 mm, simulated yield increase with early vigour associated with larger seed size was on average 4% higher compared with normal vigour wheat. Early vigour through selection of doubled early leaf sizes could increase yield by 16%. Increase in yield was mainly from increase in biomass and grain number, and was reduced at sites with seasonal rainfall plus initial soil water <300 mm. Opportunities exists for development of early vigour wheat varieties for wetter sites. Soil PAWC could play a significant role in delivering the benefit of early vigour and would require particular attention.

Improving process-based crop models to better capture genotype×environment×management interactions.

In spite of the increasing expectation for process-based crop modelling to capture genotype (G) by environment (E) by management (M) interactions to support breeding selections, it remains a challenge to use current crop models to accurately predict phenotypes from genotypes or from candidate genes. We use wheat as a target crop and the APSIM farming systems model (Holzworth et al., 2014) as an example to analyse the current status of process-based crop models with a major focus on need to improve simulation of specific eco-physiological processes and their linkage to underlying genetic controls. For challenging production environments in Australia, we examine the potential opportunities to capture physiological traits, and to integrate genetic and molecular approaches for future model development and applications. Model improvement will require both reducing the uncertainty in simulating key physiological processes and enhancing the capture of key observable traits and underlying genetic control of key physiological responses to environment. An approach consisting of three interactive stages is outlined to (i) improve modelling of crop physiology, (ii) develop linkage from model parameter to genotypes and further to loci or alleles, and (iii) further link to gene expression pathways. This helps to facilitate the integration of modelling, phenotyping, and functional gene detection and to effectively advance modelling of G×E×M interactions. While gene-based modelling is not always needed to simulate G×E×M, including well-understood gene effects can improve the estimation of genotype effects and prediction of phenotypes. Specific examples are given for enhanced modelling of wheat in the APSIM framework.

Accounting for Genotype-by-Environment Interactions and Residual Genetic Variation in Genomic Selection for Water-Soluble Carbohydrate Concentration in Wheat.

Abiotic stress tolerance traits are often complex and recalcitrant targets for conventional breeding improvement in many crop species. This study evaluated the potential of genomic selection to predict water-soluble carbohydrate concentration (WSCC), an important drought tolerance trait, in wheat under field conditions. A panel of 358 varieties and breeding lines constrained for maturity was evaluated under rainfed and irrigated treatments across two locations and two years. Whole-genome marker profiles and factor analytic mixed models were used to generate genomic estimated breeding values (GEBVs) for specific environments and environment groups. Additive genetic variance was smaller than residual genetic variance for WSCC, such that genotypic values were dominated by residual genetic effects rather than additive breeding values. As a result, GEBVs were not accurate predictors of genotypic values of the extant lines, but GEBVs should be reliable selection criteria to choose parents for intermating to produce new populations. The accuracy of GEBVs for untested lines was sufficient to increase predicted genetic gain from genomic selection per unit time compared to phenotypic selection if the breeding cycle is reduced by half by the use of GEBVs in off-season generations. Further, genomic prediction accuracy depended on having phenotypic data from environments with strong correlations with target production environments to build prediction models. By combining high-density marker genotypes, stress-managed field evaluations, and mixed models that model simultaneously covariances among genotypes and covariances of complex trait performance between pairs of environments, we were able to train models with good accuracy to facilitate genetic gain from genomic selection.

High Throughput Determination of Plant Height, Ground Cover, and Above-Ground Biomass in Wheat with LiDAR.

Crop improvement efforts are targeting increased above-ground biomass and radiation-use efficiency as drivers for greater yield. Early ground cover and canopy height contribute to biomass production, but manual measurements of these traits, and in particular above-ground biomass, are slow and labor-intensive, more so when made at multiple developmental stages. These constraints limit the ability to capture these data in a temporal fashion, hampering insights that could be gained from multi-dimensional data. Here we demonstrate the capacity of Light Detection and Ranging (LiDAR), mounted on a lightweight, mobile, ground-based platform, for rapid multi-temporal and non-destructive estimation of canopy height, ground cover and above-ground biomass. Field validation of LiDAR measurements is presented. For canopy height, strong relationships with LiDAR (r2 of 0.99 and root mean square error of 0.017 m) were obtained. Ground cover was estimated from LiDAR using two methodologies: red reflectance image and canopy height. In contrast to NDVI, LiDAR was not affected by saturation at high ground cover, and the comparison of both LiDAR methodologies showed strong association (r2 = 0.92 and slope = 1.02) at ground cover above 0.8. For above-ground biomass, a dedicated field experiment was performed with destructive biomass sampled eight times across different developmental stages. Two methodologies are presented for the estimation of biomass from LiDAR: 3D voxel index (3DVI) and 3D profile index (3DPI). The parameters involved in the calculation of 3DVI and 3DPI were optimized for each sample event from tillering to maturity, as well as generalized for any developmental stage. Individual sample point predictions were strong while predictions across all eight sample events, provided the strongest association with biomass (r2 = 0.93 and r2 = 0.92) for 3DPI and 3DVI, respectively. Given these results, we believe that application of this system will provide new opportunities to deliver improved genotypes and agronomic interventions via more efficient and reliable phenotyping of these important traits in large experiments.

Selection for water-soluble carbohydrate accumulation and investigation of genetic × environment interactions in an elite wheat breeding population.

KEY MESSAGE: Water-soluble carbohydrate accumulation can be selected in wheat breeding programs with consideration of genetic × environmental interactions and relationships with other important characteristics such as relative maturity and nitrogen concentration, although the correlation between WSC traits and grain yield is low and inconsistent. The potential to increase the genetic capacity for water-soluble carbohydrate (WSC) accumulation is an opportunity to improve the drought tolerance capability of rainfed wheat varieties, particularly in environments where terminal drought is a significant constraint to wheat production. A population of elite breeding germplasm was characterized to investigate the potential for selection of improved WSC concentration and total amount in water deficit and well-watered environments. Accumulation of WSC involves complex interactions with other traits and the environment. For both WSC concentration (WSCC) and total WSC per area (WSCA), strong genotype × environment interactions were reflected in the clear grouping of experiments into well-watered and water deficit environment clusters. Genetic correlations between experiments were high within clusters. Heritability for WSCC was larger than for WSCA, and significant associations were observed in both well-watered and water deficit experiment clusters between the WSC traits and nitrogen concentration, tillering, grains per m2, and grain size. However, correlations between grain yield and WSCC or WSCA were weak and variable, suggesting that selection for these traits is not a better strategy for improving yield under drought than direct selection for yield.

Genome-Wide Associations for Water-Soluble Carbohydrate Concentration and Relative Maturity in Wheat Using SNP and DArT Marker Arrays.

Improving water-use efficiency by incorporating drought avoidance traits into new wheat varieties is an important objective for wheat breeding in water-limited environments. This study uses genome wide association studies (GWAS) to identify candidate loci for water-soluble carbohydrate accumulation-an important drought-avoidance characteristic in wheat. Phenotypes from a multi-environment trial with experiments differing in water availability and separate single nucleotide polymorphism (SNP) and diversity arrays technology (DArT) marker sets were used to perform the analyses. Significant associations for water-soluble carbohydrate accumulation were identified on chromosomes 1A, 1B, 1D, 2D, and 4A. Notably, these loci did not collocate with the major loci identified for relative maturity. Loci on chromosome 1D collocated with markers previously associated with the high molecular weight glutenin Glu-D1 locus. Genetic × environmental interactions impacted the results strongly, with significant associations for carbohydrate accumulation identified only in the water-deficit experiments. The markers associated with carbohydrate accumulation may be useful for marker-assisted selection of drought tolerance in wheat.

Methodology for High-Throughput Field Phenotyping of Canopy Temperature Using Airborne Thermography.

Lower canopy temperature (CT), resulting from increased stomatal conductance, has been associated with increased yield in wheat. Historically, CT has been measured with hand-held infrared thermometers. Using the hand-held CT method on large field trials is problematic, mostly because measurements are confounded by temporal weather changes during the time required to measure all plots. The hand-held CT method is laborious and yet the resulting heritability low, thereby reducing confidence in selection in large scale breeding endeavors. We have developed a reliable and scalable crop phenotyping method for assessing CT in large field experiments. The method involves airborne thermography from a manned helicopter using a radiometrically-calibrated thermal camera. Thermal image data is acquired from large experiments in the order of seconds, thereby enabling simultaneous measurement of CT on potentially 1000s of plots. Effects of temporal weather variation when phenotyping large experiments using hand-held infrared thermometers are therefore reduced. The method is designed for cost-effective and large-scale use by the non-technical user and includes custom-developed software for data processing to obtain CT data on a single-plot basis for analysis. Broad-sense heritability was routinely >0.50, and as high as 0.79, for airborne thermography CT measured near anthesis on a wheat experiment comprising 768 plots of size 2 × 6 m. Image analysis based on the frequency distribution of temperature pixels to remove the possible influence of background soil did not improve broad-sense heritability. Total image acquisition and processing time was ca. 25 min and required only one person (excluding the helicopter pilot). The results indicate the potential to phenotype CT on large populations in genetics studies or for selection within a plant breeding program.

Do wheat breeders have suitable genetic variation to overcome short coleoptiles and poor establishment in the warmer soils of future climates?

Increases in air and soil temperatures will impact cereal growth and reduce crop yields. Little is known about how increasing temperatures will impact seedling growth and crop establishment. Climate forecast models predict that by 2060, mean and maximum air temperatures in the Australian wheatbelt will increase by 2-4°C during the March-June sowing period, and particularly at lower latitudes. Concomitant increases in soil temperature will shorten coleoptile length to reduce crop establishment, particularly where deep sowing to access sub-surface moisture. Mean coleoptile length was reduced in commercial wheat (Triticum aestivum L.) germplasm with increasing soil temperature (106mm and 51mm at 15°C and 31°C, respectively). Coleoptile lengths of modern semidwarf varieties were significantly (P<0.01) shorter than those of older tall wheats at 15°C (95mm and 135mm) and 31°C (46mm and 70mm). A 12-parent diallel indicated large additive and small non-maternal genetic effects for coleoptile length at 15°C and 27°C. Large genotype rank changes for coleoptile length across temperatures (rs=0.37, P<0.05) contributed to smaller entry-mean heritabilities (0.41-0.67) to reduce confidence in selection for long-coleoptile genotypes across contrasting temperatures. General combining ability effects were strongly correlated across temperatures (rp=0.81, P < 0.01), indicating the potential of some donors in identification of progeny with consistently longer coleoptiles. Warmer soils in future will contribute to poor establishment and crop failure, particularly with deep-sown semidwarf wheat. Breeding long-coleoptile genotypes with improved performance will require targeted selection at warmer temperatures in populations incorporating novel sources of reduced height and greater coleoptile length.

Integration of phenotyping and genetic platforms for a better understanding of wheat performance under drought.

Identifying markers for physiological traits of proven value in breeding, especially ones that are consistent across environments with different patterns of stress, strengthens the toolkit to increase confidence in the value and delivery from physiological breeding. To identify markers relevant to drought adaptation, this review will highlight the importance of development and implementation of robust and repeatable phenotyping that is relevant to the different target drought types, and practical examples of managed environment facilities in Australia and Mexico are given. These facilities can be used as models to: (i) improve reliability and consistency of environments and genetic responses to the environment at a global scale; (ii) improve the capacity to deliver quantitative trait loci (QTLs) as user-friendly markers for enriching populations; and (iii) illustrate the use of populations with a narrow range of variation for phenology allowing the identification of QTLs for drought-adaptive traits. However, the importance of further optimizing phenology and plant height at a global scale is highlighted. Finally, the impact of physiological trait-based crossing is demonstrated and supports the need for urgent development of robust genetic markers.

Plot size matters: interference from intergenotypic competition in plant phenotyping studies.

Genetic and physiological studies often comprise genotypes diverse in vigour, size and flowering time. This can make the phenotyping of complex traits challenging, particularly those associated with canopy development, biomass and yield, as the environment of one genotype can be influenced by a neighbouring genotype. Limited seed and space may encourage field assessment in single, spaced rows or in small, unbordered plots, whereas the convenience of a controlled environment or greenhouse makes pot studies tempting. However, the relevance of such growing conditions to commercial field-grown crops is unclear and often doubtful. Competition for water, light and nutrients necessary for canopy growth will be variable where immediate neighbours are genetically different, particularly under stress conditions, where competition for resources and influence on productivity is greatest. Small hills and rod-rows maximise the potential for intergenotypic competition that is not relevant to a crop's performance in monocultures. Response to resource availability will typically vary among diverse genotypes to alter genotype ranking and reduce heritability for all growth-related traits, with the possible exception of harvest index. Validation of pot experiments to performance in canopies in the field is essential, whereas the planting of multirow plots and the simple exclusion of plot borders at harvest will increase experimental precision and confidence in genotype performance in target environments.