Climate change determined drought stress profiles in rainfed common bean production systems in Brazil
Introduction
Beans are crucial for food security in much of Latin America and East and Southern Africa, as they are rich in protein and essential vitamins and minerals (Beebe, 2012, Broughton et al., 2003). Common bean (Phaseolus vulgaris L.) is the most widely grown and consumed grain legume in the world and plays an especially significant role in the human diet (Khoury et al., 2014). It is cultivated in a wide range of production systems, representing different climates, soils, cultivars and levels of technology.
In Brazil, currently the largest single bean producer in the world (∼2.5 million tons in 2013, FAOSTAT, 2015, IBGE, 2015), common bean is a staple crop and constitutes a primary source of protein in the diet of the Brazilian population (per capita consumption estimated at 17.8 kg year−1) (Embrapa Arroz e Feijão, 2015). Some 93% (2.8 million ha) of common bean Brazilian production area is under rainfed conditions (IBGE, 2015). In the Goiás State, one of the main bean-producing states in Brazil, crop production is concentrated in the same geographic area, but spread across three growing seasons, namely, wet (sowing from 1 Nov to 31 Dec), dry (sowing from 1 Jan to 28 Feb) and winter (sowing from 1 May to 30 Jun). Due to the mix of agro-environmental conditions and varietal performance in these seasons, they are also referred to as Target Population of Environments (TPEs, Heinemann et al., 2016). Rainfed bean production − the focus of this paper, occurs mainly in the wet and dry TPEs. Observed yields at farmer level in these TPEs are, on average, 1700 (wet) and 1500 kg ha−1 (dry); these are low compared to the winter TPE (2700 kg ha−1) (IBGE, 2015).
Changes in climate have already been reducing global agricultural production by 1–5% per decade over the last 30 years and will continue to pose challenges to agriculture in the coming decades (Challinor et al., 2014, Porter et al., 2014). Reductions in agricultural productivity with consequences for food security associated to climate change are expected in the absence of adaptation in many parts of South America (Magrin and Marengo, 2014). Bean production systems are no exception to these impacts; modelling studies have projected a systematic decrease in the climatic suitability for common beans cultivation across most of South America (including the Goiás state), with heat and drought stresses being the key drivers for such suitability reductions (Beebe et al., 2011, Ramirez-Cabral et al., 2016).
Cultivar adaptation has been shown to be the most effective adaptation measure for reducing vulnerability to climate change (Challinor et al., 2014), while also promoting sustainable development by for example helping to sustainably close yield gaps (Cassman, 1999, van Ittersum et al., 2016). Hence, targeted breeding programs that develop novel climate-adapted varieties in an anticipated manner can substantially contribute to cropping system adaptation, including improving the tolerance to both biotic and abiotic stresses (McClean et al., 2011, Challinor et al., 2016, Rippke et al., 2016). Nevertheless, for breeding programs to successfully establish priorities under climate change, breeders need to identify and characterize (in frequency and intensity) which stresses are most important and how during the 21st century these stresses change across space and time.
Here, we identify and characterize drought stress profiles for the rainfed common bean TPEs (wet, dry) in the Goiás state, and assess their changes under climate change scenarios by 2030 (as derived from the CMIP5 ensemble). Our ultimate aim is to identify potential future breeding directions for rainfed common beans in Goiás state during the 21st century. Our analysis is the first assessment of breeding priorities for common bean, and contributes important new evidence to a growing body of literature on breeding priorities under climate change.
Section snippets
Current and future weather data
Observed historical weather data for 26 weather stations available within the study region (Fig. 1, Supplementary Table S1) for the period 1980–2005 were gathered from a previous study (Heinemann et al., 2016). The dataset consists of daily observations from the Brazilian Meteorological Institute (INMET, http://www.inmet.gov.br) for which gaps and errors had been thoroughly checked and corrected as described in Heinemann et al. (2016) and D’Afonseca et al. (2012, D’Afonseca et al., 2013a,
Projected changes in seasonal temperature, precipitation and crop yield
Projected changes in precipitation and temperature are shown in Fig. 2, Fig. 3 for the wet and dry TPEs (respectively), for all RCPs, for the period 2020–2045, relative to 1980–2005. The ensemble mean temperature increases for the wet (dry) TPE ranged from 1.10 to 1.66 °C (1.03–1.59 °C) across all RCPs, with RCP 8.5 showing the largest warming. The largest temperature increases are projected to occur in the southwest region of the study area (RCP 8.5), the largest grain production region for both
Climate change impacts across environment groups
This study uses environmental classification based on simulated yields for historical and near future (2020–2045) climatic data set on two distinct rainfed common beans target population environments (wet and dry TPEs) in the Goiás State (Brazil), for determining the frequency of environment groups in the near future.
Historically, the main common bean production region for both TPEs is in the south of the state (Supplementary Fig. S6–S7) mainly due to cooler temperatures (wet TPE) and a longer
Acknowledgments
JRV is supported by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), which is carried out with support from CGIAR Fund Donors and through bilateral funding agreements. For details please visit https://ccafs.cgiar.org/donors. The views expressed in this document cannot be taken to reflect the official opinions of these organizations. We acknowledge the World Climate Research Program’s Working Group on Coupled Modelling, which is responsible for CMIP, and we
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