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Plant sciences, sustainable farming systems and food quality
RESEARCH ARTICLE

Assessment of crop-management strategies to improve soybean resilience to climate change in Southern Brazil

Rafael Battisti A G , Paulo C. Sentelhas B , Phillip S. Parker C , Claas Nendel C , Gil M. De S. Câmara D , José R. B. Farias E and Claudir J. Basso F
+ Author Affiliations
- Author Affiliations

A College of Agronomy, Federal University of Goiás, Avenida Esperança, Goiânia, GO 74690-900, Brazil.

B Department of Biosystems Engineering, ESALQ, University of São Paulo, Avenida Pádua Dias, Piracicaba, SP 13418-900, Brazil.

C Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Straße, 15374 Müncheberg, Germany.

D Department of Plant Production, ESALQ, University of São Paulo, Avenida Pádua Dias, Piracicaba, SP 13418-900, Brazil.

E National Soybean Research Center, Embrapa, Caixa Postal 231, Londrina, PR 86001-970, Brazil.

F Department of Agricultural and Environmental Sciences, Federal University of Santa Maria, Linha 7 de Setembro, Frederico Westphalen, RS 98400-000, Brazil.

G Corresponding author. Email: battisti@ufg.br

Crop and Pasture Science 69(2) 154-162 https://doi.org/10.1071/CP17293
Submitted: 28 April 2017  Accepted: 14 November 2017   Published: 31 January 2018

Abstract

Management is the most important handle to improve crop yield and resilience under climate change. The aim of this study was to evaluate how irrigation, sowing date, cultivar maturity group and planting density can contribute for increasing the resilience of soybean (Glycine max (L.) Merr.) under future climate in southern Brazil. Five sites were selected to represent the range of Brazilian production systems typical for soybean cultivation. Yields were obtained from a crop-model ensemble (CROPGRO, APSIM and MONICA). Three climate scenarios were evaluated: baseline (1961–2014), and two future climate scenarios for the mid-century (2041–70) with low (+2.2°C, A1BLs) and high (+3.2°C, A1BHs) deltas for air temperature and with atmospheric [CO2] of 600 ppm. Supplementary irrigation resulted in higher and more stable yields, with gains in relation to a rainfed crop of 543, 719, 758 kg ha–1, respectively, for baseline, A1BLs and A1BHs. For sowing date, the tendencies were similar between climate scenarios, with higher yields when soybean was sown on 15 October for each simulated growing season. Cultivar maturity group 7.8 and a plant density of 50 plants m−2 resulted in higher yields in all climate scenarios. The best crop-management strategies showed similar tendency for all climate scenarios in Southern Brazil.

Additional keywords: adaptation strategies, crop cycle, simulation model, sowing window, soybean yield.


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