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.