No abstract

Brachiaria and Cynodon are important pasture grasses in Brazil. Convert HD 364 (Dow AgroSciences, São Paulo, Brazil) brachiariagrass (Brachiaria hybrid CIAT 36087; also known as Mulato II) is a new hybrid released for use in a broad range of environments. It has high nutritive value and yield, but there are no year‐round comparisons, including the dry season, with other pasture grasses. Forage accumulation and crude protein (CP), neutral detergent fiber (NDF), and in vitro digestible organic matter (IVDOM) concentrations were evaluated for Convert HD 364, Marandu palisadegrass {B. brizantha (Hochst. ex A. Rich.) R. D. Webster [syn. Urochloa brizantha (A. Rich.) Stapf]; CIAT 6297}, and Tifton 85 bermudagrass (Cynodon spp.) during dry and rainy seasons from 2011 to 2013. Irrigated and rainfed plots were harvested every 28 and 42 d. Convert HD 364 had similar or greater forage accumulation (17.9–22.8 Mg DM ha−1) than Marandu and Tifton 85 (15% greater than Marandu and 12% greater than Tifton 85 when irrigated and harvested every 28 d). Tifton 85 CP concentration was 140 g kg−1, greater than that of the other grasses when harvested every 28 d and irrigated. Convert HD 364 (537 g kg−1) NDF concentration was least, regardless of irrigation, harvest frequency, or season. This was associated with IVDOM concentration greater than 650 g kg−1, similar to that of Marandu. Convert HD 364 is a viable option for diversification of pasture‐based animal production systems in tropical areas due to high forage accumulation and nutritive value when fertilized and well managed.

Crop improvement efforts aiming at increasing crop production (quantity, quality) and adapting to climate change have been subject of active research over the past years. But, the question remains 'to what extent can breeding gains be achieved under a changing climate, at a pace sufficient to usefully contribute to climate adaptation, mitigation and food security?'. Here, we address this question by critically reviewing how model-based approaches can be used to assist breeding activities, with

Wheat (Triticum aestivum) is the most widely grown food crop in the world threatened by future climate change. In this study, we simulated climate change impacts and adaptation strategies for wheat globally using new crop genetic traits (CGT), including increased heat tolerance, early vigor to increase early crop water use, late flowering to reverse an earlier anthesis in warmer conditions, and the combined traits with additional nitrogen (N) fertilizer applications, as an option to maximize genetic gains. These simulations were completed using three wheat crop models and five Global Climate Models (GCM) for RCP 8.5 at mid-century. Crop simulations were compared with country, US state, and US county grain yield and production. Wheat yield and production from high-yielding and low-yielding countries were mostly captured by the model ensemble mean. However, US state and county yields and production were often poorly reproduced, with large variability in the models, which is likely due to poor soil and crop management input data at this scale. Climate change is projected to decrease global wheat production by −1.9% by mid-century. However, the most negative impacts are projected to affect developing countries in tropical regions. The model ensemble mean suggests large negative yield impacts for African and Southern Asian countries where food security is already a problem. Yields are predicted to decline by −15% in African countries and −16% in Southern Asian countries by 2050. Introducing CGT as an adaptation to climate change improved wheat yield in many regions, but due to poor nutrient management, many developing countries only benefited from adaptation from CGT when combined with additional N fertilizer. As growing conditions and the impact from climate change on wheat vary across the globe, region-specific adaptation strategies need to be explored to increase the possible benefits of adaptations to climate change in the future.

Forage-based livestock systems are complex, and interactions among animals, plants and the environment exist at several levels of complexity, which can be evaluated using computer modelling. Despite the importance of grasslands for livestock production in Brazil, tools to assist producers to make decisions in forage-livestock systems are scarce. The objective of this research was to use the CROPGRO-Perennial Forage model to simulate the irrigated and rainfed growth of Marandu palisade grass (Brachiaria brizantha (A. Rich.) Stapf. cv. Marandu), the most widely grown forage in Brazil, by using parameters previously calibrated for the tall-growing cv. Xaraes of the same species, under non-limiting water conditions. The model was calibrated for the irrigated experiment and then tested against independent data of the rainfed experiment. Data used to calibrate the model included forage production, plant-part composition, leaf photosynthesis, leaf area index, specific leaf area, light interception and plant nitrogen (N) concentration from a field experiment conducted during 2011-13 in Piracicaba, SP, Brazil. Agronomic and morpho-physiological differences between the two grasses, such as maximum leaf photosynthesis, N concentration and temperature effect on growth rate, were considered in the calibration. Under rainfed conditions, the simulations using the Penman-Monteith FAO 56 method gave a more realistic water stress response than the Priestley and Taylor method. After model parameterisation, the mean simulated herbage yield was 4582 and 5249 kg ha -1 for 28 days and 42 days irrigated, and 4158 and 4735 kg ha -1 for 28 days and 42 days rainfed, respectively. The root-mean-square error ranged from 464 to 526 kg ha -1 and the D-statistic from 0.907 to 0.962. The simulated/observed ratios ranged from 0.977 to 1.001. These results suggest that the CROPGRO-Perennial Forage model can be used to simulate growth of Marandu palisade grass adequately under irrigated and rainfed conditions.

No abstract

I n the global gridded crop model (GGCM) approach, the world is divided into grid cells defined by latitude and longitude, and crop yield for the landmass in each grid cell is simulated 1-4 . To estimate the potential impact of climate change on food produc tion, researchers aggregate simulated results into nations, regions or the world to aid economic analysis and inform policymaking at different scales 5,6 . To be deemed trustworthy, GGCM results must provide accurate estimates of yield-climate relationships, or other wise give explicit information of the uncertainty of projections. Current crop models produce different results due to underlying differences in climate projections, model structure, inputs and parameterization, so overall there is a large degree of uncertainty in crop yield projections 1 . An effective way to quantify this uncer tainty is to compare multiple climate-crop simulations of the same climate change problem 7,8 . Most current impact assessments are conducted for just a few wellcharacterized sites 9-12 , so while model accuracy can be improved through understanding where and how uncertainty arises in a multimodel ensemble 13 , the uncertainty of predictions across the mass of diverse arable lands around the world is difficult to estimate.Estimating impact consistently across the globe by applying a GGCM ensemble is expensive and difficult in terms of labour, tim ing, computational ability and resources, and expertise. Compared with crop modelling at individual sites, GGCMs introduce additional sources of uncertainty such as those that arise from geospatial data and data processing. For example, recent GGCM ensemble studies compared simulation results from different research groups 14 . Even when committed to following a common simulation protocol 15 , groups adopted their unique ways of processing data and param eterizing the models for the globe. A further aspect that has received insufficient attention in GGCMs is model parameterization and how it can affect the uncertainty of globalscale predictions, par ticularly for parameters describing localscale spatial variation in the use and performance of cultivars, varieties or hybrids.Here we present a range of global wheat yield responses to future warming scenarios based on a large simulation ensemble. The ensemble was composed of 1,440 global simulations, with combina tions of 20 climate projections (5 climate models under 4 representa tive concentration pathways (RCPs) for greenhouse gas emissions), 3 crop models, 4 parameterization strategies and 3 management inputs of sowing date ( Supplementary Fig. 1). We linked the uncer tainty of gridded predictions to the latitude of grid cells. To include the uncertainty of crop yield predictions due to CO 2 fertilization, we conducted two sets of simulations: the first accounted for the effects of both increased CO 2 as projected by the RCPs and changes in climate (CC w/CO 2 ); the second accounted for changes in climate at 360 ppm CO 2 , assuming CO 2 concentration would remain constant at its...

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 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 ~half of the global wheat growing area.