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All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. S ugarcane is of major social and economic importance in Brazil. It is one of the most important commodities in Brazilian agribusiness, contributing to the energy and food security of the country, as sugar, ethanol, and biomass for energy are produced from sugarcane (Goldemberg, 2007). Brazilian production of sugarcane has been expanding since the early 2000s, now reaching areas where it has never been planted, mainly driven by the rise in ethanol consumption in the internal market. The coefficient of variation of annual national production was 7.2% from 1990 to 2008, ranging from 5 to 12% among different regions. The new production areas, mainly located in Central and Northeast regions are more often subject to higher risk. Crop simulation models may contribute to improved crop monitoring and yield forecasting, while enhancing our understanding of sugarcane growth and yield. Worldwide, there are several models dedicated to sugarcane crop simulation: AUSCANE (

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This study evaluated the effects of climate change on sugarcane yield, water use efficiency, and irrigation needs in southern Brazil, based on downscaled outputs of two general circulation models (PRECIS and CSIRO) and a sugarcane growth model. For three harvest cycles every year, the DSSAT/CANEGRO model was used to simulate the baseline and four future climate scenarios for stalk yield for the 2050s. The model was calibrated for the main cultivar currently grown in Brazil based on five field experiments under several soil and climate conditions. The sensitivity of simulated stalk fresh mass (SFM) to air temperature, CO 2 concentration [CO 2 ] and rainfall was also analyzed. Simulated SFM responses to [CO 2 ], air temperature and rainfall variations were consistent with the literature. There were increases in simulated SFM and water usage efficiency (WUE) for all scenarios. On average, for the current sugarcane area in the State of São Paulo, SFM would increase 24 % and WUE 34 % for rainfed sugarcane. The WUE rise is relevant because of the current concern about water supply in southern Brazil. Considering the current technological improvement rate, projected yields for 2050 ranged from 96 to 129 tha −1 , which are respectively 15 and 59 % higher than the current state average yield.Climatic Change (2013) 117:227-239 DOI 10.1007/s10584-012-0561-

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A necessary initial step in assessing the value of climate information for regional agriculture is to gauge user perceptions concerning the use of that information. We attempt to do so for cereal and oilseed production in Pergamino, Argentina, located in the Pampas, one of the world's major agricultural regions. A survey of 200 farmers identified climate forecast scale and the reliability of the source of forecast as critical obstacles to adoption. Users' incomplete knowledge of how El Niño-Southern Oscillation affects their region may also pose an obstacle to greater use of climate information. A related problem is that users sometimes confuse the different time scales of weather and climate forecasting. Research and outreach to downscaling forecasts temporally and spatially toward user communities would help close the gap of expectations between forecast user and provider, and would facilitate the trust building process between the two that must precede adoption. KEY WORDS: Climate information · Attitudes · ENSO · AgricultureResale or republication not permitted without written consent of the publisher Clim Res 19: 57-67, 2001 priorities and may undermine our ability to provide useful information. Scientists need to know how the public is likely to respond to ENSO-based climate forecasts because those responses alter the economic influence of climatic impacts (Burns 1999). Policy makers should understand user needs, to realize the potential economic value from the emerging technology of ENSO-based climate forecasting (Changnon 1996).To assess perceptions of ENSO-based climatic forecasting, we used focus groups and a user survey to approach cereal and oilseed producers in Pergamino, Argentina, located in the climatically favorable eastern portion of the Pampas, one of the world's major agricultural regions (Fig. 1). We focus on Argentina for several reasons. Argentina is a major agricultural producer. The value of its agricultural exports was 50 to 60% that of its overall exports and 5.5 to 9.6% of its GDP (gross domestic product) over 1989 (InterAmerican Development Bank 1999. In Argentina, interannual climatic variability causes high variability in crop yields and returns (Parellada et al. 1998, Messina et al. 1999, Podestá et al. 1999a, Ferreyra et al. 2001.1 Since the economic reforms of 1991, rising grain prices, relative to those for beef, have induced an expansion of cultivated areas that amplifies the effects of anomalous climate (Basualdo 1995). 2 The predominant soil in Pergamino is a typical Argiudoll (Paruelo & Sala 1993). Characteristic crop rotations include maize, soybean, and a wheat-soybean relay. Median annual precipitation is 937 mm. Hall et al. (1992) give a description of the climate, soils, and crop production systems in the Pampas. We take an empirical case study approach so that we can identify a specific context for climate information in agricultural decision making. However, the similarity in production scale, crops grown and technology in the Pampas to those in other ma...

Crop models are widely used in agricultural impact studies. However, many studies have reported large uncertainties from single-model-based simulation analyses, suggesting the need for multi-model simulation capabilities. In this study, the APSIM-Nwheat model was integrated into the Decision Support System for Agro-technology (DSSAT), which already includes two wheat models, to create multi-model simulation capabilities for wheat cropping systems analysis. The new model in DSSAT (DSSAT-Nwheat) was evaluated using more than 1,000 observations from field experiments of 65 treatments, which included a wide range of nitrogen fertilizer applications, water supply (irrigation and rainout shelter), planting dates, elevated atmospheric CO 2 concentrations, temperature variations, cultivars, and soil types in diverse climatic regions that represented the main wheat growing areas of the world. DSSAT-Nwheat reproduced the observed grain yields well with an overall root mean square deviation (RMSD) of 0.89 t/ha (13%). Nitrogen applications, water supply, and planting dates had large effects on observed biomass and grain yields, and the model reproduced these crop responses well. Crop total biomass and nitrogen uptake were reproduced well despite relatively poor simulations of observed leaf area measurements during the growing season. The low sensitivity of biomass simulations to poor simulations of leaf area index (LAI) were due to little changes in intercepted solar radiation at LAI >3 and water and nitrogen stress often limiting photosynthesis and growth rather than light interception at low LAI. The responses of DSSAT-Nwheat to temperature variations and elevated atmospheric CO 2 concentrations were close to observed responses. When compared with the two other DSSAT-wheat models (CERES and CROPSIM), these responses were similar, except for the responses to hot environments, due to different approaches in modeling heat stress effects.

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