Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment.

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.

Harnessing translational research in wheat for climate resilience.

Despite being the world's most widely grown crop, research investments in wheat (Triticum aestivum and Triticum durum) fall behind those in other staple crops. Current yield gains will not meet 2050 needs, and climate stresses compound this challenge. However, there is good evidence that heat and drought resilience can be boosted through translating promising ideas into novel breeding technologies using powerful new tools in genetics and remote sensing, for example. Such technologies can also be applied to identify climate resilience traits from among the vast and largely untapped reserve of wheat genetic resources in collections worldwide. This review describes multi-pronged research opportunities at the focus of the Heat and Drought Wheat Improvement Consortium (coordinated by CIMMYT), which together create a pipeline to boost heat and drought resilience, specifically: improving crop design targets using big data approaches; developing phenomic tools for field-based screening and research; applying genomic technologies to elucidate the bases of climate resilience traits; and applying these outputs in developing next-generation breeding methods. The global impact of these outputs will be validated through the International Wheat Improvement Network, a global germplasm development and testing system that contributes key productivity traits to approximately half of the global wheat-growing area.

Breeder friendly phenotyping.

The word phenotyping can nowadays invoke visions of a drone or phenocart moving swiftly across research plots collecting high-resolution data sets on a wide array of traits. This has been made possible by recent advances in sensor technology and data processing. Nonetheless, more comprehensive often destructive phenotyping still has much to offer in breeding as well as research. This review considers the 'breeder friendliness' of phenotyping within three main domains: (i) the 'minimum data set', where being 'handy' or accessible and easy to collect and use is paramount, visual assessment often being preferred; (ii) the high throughput phenotyping (HTP), relatively new for most breeders, and requiring significantly greater investment with technical hurdles for implementation and a steeper learning curve than the minimum data set; (iii) detailed characterization or 'precision' phenotyping, typically customized for a set of traits associated with a target environment and requiring significant time and resources. While having been the subject of debate in the past, extra investment for phenotyping is becoming more accepted to capitalize on recent developments in crop genomics and prediction models, that can be built from the high-throughput and detailed precision phenotypes. This review considers different contexts for phenotyping, including breeding, exploration of genetic resources, parent building and translational research to deliver other new breeding resources, and how the different categories of phenotyping listed above apply to each. Some of the same tools and rules of thumb apply equally well to phenotyping for genetic analysis of complex traits and gene discovery.

Optimizing Winter Wheat Resilience to Climate Change in Rain Fed Crop Systems of Turkey and Iran.

Erratic weather patterns associated with increased temperatures and decreasing rainfall pose unique challenges for wheat breeders playing a key part in the fight to ensure global food security. Within rain fed winter wheat areas of Turkey and Iran, unusual weather patterns may prevent attaining maximum potential increases in winter wheat genetic gains. This is primarily related to the fact that the yield ranking of tested genotypes may change from one year to the next. Changing weather patterns may interfere with the decisions breeders make about the ideotype(s) they should aim for during selection. To inform breeding decisions, this study aimed to optimize major traits by modeling different combinations of environments (locations and years) and by defining a probabilistic range of trait variations [phenology and plant height (PH)] that maximized grain yields (GYs; one wheat line with optimal heading and height is suggested for use as a testing line to aid selection calibration decisions). Research revealed that optimal phenology was highly related to the temperature and to rainfall at which winter wheat genotypes were exposed around heading time (20 days before and after heading). Specifically, later winter wheat genotypes were exposed to higher temperatures both before and after heading, increased rainfall at the vegetative stage, and reduced rainfall during grain filling compared to early genotypes. These variations in exposure to weather conditions resulted in shorter grain filling duration and lower GYs in long-duration genotypes. This research tested if diversity within species may increase resilience to erratic weather patterns. For the study, calculated production of a selection of five high yielding genotypes (if grown in five plots) was tested against monoculture (if only a single genotype grown in the same area) and revealed that a set of diverse genotypes with different phenologies and PHs was not beneficial. New strategies of progeny selection are discussed: narrow range of variation for phenology in families may facilitate the discovery and selection of new drought-resistant and avoidant wheat lines targeting specific locations.

Aveia preta e azevém anual colhidos por interceptação de luz ou intervalo fixo de tempo em sistemas integrados de agricultura e pecuária no Estado de São Paulo / Black oats and annual ryegrass harvested by light interception or fixed rest interval in integrated crop-livestock systems in São Paulo State, Brazil

Objetivou-se caracterizar o potencial produtivo, a distribuição mensal, a composição morfológica e botânica da forragem de aveia preta e de azevém anual submetidos a estratégias de manejo de corte no estado de São Paulo. O delineamento experimental foi inteiramente casualizado, com tratamentos correspondendo a combinações entre formas de cultivo (aveia e azevém exclusivos ou combinados) e estratégias de colheita (cortes com 95% de interceptação de luz (IL) ou intervalo FIXO de 30 dias de rebrotação), em arranjo fatorial com três repetições. Os estandes colhidos por IL acumularam 1580kg MS ha-1 corte-1, e os por FIXO 2020kg de MS ha-1 corte-1. A proporção de folhas foi maior no azevém (70%), seguido do cultivo combinado (64%) e depois pela aveia (52%). Entre os manejos, a IL resultou em maiores proporções de folha e menor proporção de colmos que o FIXO. A proporção de azevém foi de 64%, sob o manejo por IL, e 70% no manejo por descanso FIXO. Ao longo do tempo, ocorreu a substituição da aveia pelo azevém na composição do dossel combinado. Ambas as estratégias de manejo podem ser adotadas para essas forrageiras de inverno em sistemas de integração agricultura e pecuária, com vantagem para o azevém.

The objective in this study was to characterize the productive potential, monthly yield distribution, and forage plant-part and botanical composition of black oats and annual ryegrass under harvest strategies in São Paulo. The experimental design was completely randomized with treatments corresponding to the combination among stand composition (exclusive oats, exclusive annual ryegrass, or oats + ryegrass, O+R) and harvest strategies (at 95% LI or every 30 days of regrowth, FIXED), in a factorial arrangement with three replications. Stands managed by LI accumulated 1580kg DM ha-1 harvest-1, and those managed by FIXED 2020kg DM ha-1 harvest-1. Leaf proportion was higher in ryegrass (70%), followed by O+R (64%) and oat (52%). Swards managed by LI had higher proportion of leaves and less stem than those harvested at FIXED intervals. The proportion of ryegrass was higher under the LI management (64%), than under the FIXED (70%). Over time there was the replacement of oats by ryegrass in the mixed canopy composition. Exclusive ryegrass stands are recommended. Both harvest strategies can be adopted for these winter forages in integrated systems of agriculture and livestock.