Environment quality, sowing date, and genotype determine soybean yields in the Argentinean Gran Chaco
Introduction
The global demand for soybean continues growing (Barret, 2019). This demand is met by increased production levels per cropped area but also by cropland expansion. Argentina currently produces 16 % of global soybean production (FAO, 2019), and it is the largest exporter of high protein soybean meal (USDA-FAS, 2020). This was possible because of both an increase in soybean acreage in the central temperate region (between latitudes 30 and 38 °S) and the expansion of soybean into marginal areas (Aizen et al., 2009; Viglizzo et al., 2011; Ray et al., 2012). Northeastern of Argentina (integrated by Formosa, Chaco, eastern Santiago del Estero, and northern Santa Fe provinces) is part of the Gran Chaco region, and has experienced one of the highest rates of deforestation for agricultural purposes since the mid-90’s (Vallejos et al., 2015; Piquer-Rodriguez et al., 2015; Volante et al., 2016). Currently these new croplands produce 5.3 million tons of soybean in 1.9 million hectares, representing 10 and 12 % of total Argentinean soybean production and cropped area, respectively (MAGYP, 2019). Soybean production from this region receives considerable attention due to the connection between deforestation, soybean production, and the use of its soybean meal for the production of European meat and dairy products (Fehlenberg et al., 2017; Goñi, 2018). Despite its regional and international relevance, little attention has been put on the management and environmental drivers of soybean productivity in this region. These studies are needed to optimize soybean management and to help improve the sustainability indicators of Argentinean soybean production.
Key environment and management variables affecting soybean yield were identified under different production systems worldwide, but there is no record of such studies in areas with similar agroecologic characteristics to the Argentinean Gran Chaco. Environmental variables that affect soybean yield usually involve climatic factors like water availability and temperature (Ashley and Ethridge, 1978; Prasad et al., 2008; Baker et al., 1989; Boote et al., 2005; Andrade and Satorre, 2015), radiation (Rattalino-Edreira et al., 2020), soil type (Di Mauro et al., 2018), and several edaphic parameters like soil organic matter or pH (Bacigaluppo et al., 2011; Smidt et al., 2016), among others. Management variables involve agronomic options controlled by farmers, like the selection of sowing date and crop genotype (Egli and Cornelius, 2009; Mourtzinis et al., 2017; Vitantonio-Mazzini et al., 2021), crop rotation scheme, row spacing, stand density, and sowing quality (Di Mauro et al., 2019; Masino et al., 2018), fertilization (Calviño and Sadras, 1999), and fungicide use (Di Mauro et al., 2018). Additionally, interactions between genotype, management, and environmental variables are common (Wilson et al., 2014; Salmerón et al., 2016; Goldman et al., 1989; Zhou et al., 2016). In our study region, for example, maturity groups (MGs, or crop cycle length) VII and VIII have been replaced by shorter MGs V and VI during the last ten years. However, there is no scientific evidence that this replacement has been beneficial across environments or sowing dates. The identification of key predictor variables affecting soybean yield can reveal the importance of farmers’ decisions, and provide significant insights towards a more efficient and sustainable management on these new croplands. Soil organic carbon in the Argentinean Gran Chaco is decreasing (Baldassini and Paruelo, 2020), but none of the previous studies have tested, for example, the existence of crop yield penalties related to the number of years after land conversion to agriculture nor the yield effect of reduced soil organic carbon stocks.
For our study region in the Gran Chaco, Aramburu-Merlos et al. (2015) revealed that current soybean yield levels are 60 % of water limited yield potential, so there is room for farmers to exploit management x environment interactions to close exploitable yield gaps and increase regional production (Hatfield and Walthall, 2015). For this purpose, identifying specific crop yield predictors is required to assist farmers in their decision making processes (Gambin et al., 2016; Tittonell et al., 2008). Interestingly, there is no record of documented studies aimed to identify and quantify soybean yield drivers in the region.
Our general objective was to evaluate the influence of different management and environmental variables on soybean grain yield in the Argentinean Gran Chaco. Specific objectives were (i) to identify the most important management and environmental predictors for soybean grain yield, and quantify the magnitude of their effect (objective 1), and (ii) to explore genotype MG interactions (objective 2), since farmers are sowing MGs V to VIII genotypes without a solid understanding of their relative performance across changing environments or managements. For this purpose, we analyzed 112 trials conducted in farmers’ fields during eleven growing seasons.
Section snippets
Description of the on-farm experiments
A total number of 112 trials were conducted during eleven growing seasons, from 2008/09 to 2018/19, distributed within the Argentinean Gran Chaco region. These trials were part of a multi-environmental trial (MET) network conducted by the non-governmental organization AAPRESID (No-till Argentinean Farmers Association) and Las Breñas experimental station of the National Institute of Agricultural Technology (INTA; Fig. 1). METs consist of growing a set of genotypes across a large number of farmer
Management and environmental predictors of soybean yield
To test which are the most relevant management and environmental predictors for soybean yield in our study region we analyzed the yield of a single genotype grown in 106 sites during 11 seasons. The wide range of management and environmental variables explored led to yields varying from 435 to 5117 kg ha−1 across sites (Fig. 2). This wide variability allowed to adequately test the yield effect of a large number of relevant predictors. Several predictors that showed apparent association with
Discussion
Our study identified key soybean yield predictors, and quantified their influence, in our study region within the Argentinean Gran Chaco. To our knowledge this is the first analysis addressing these goals for soybean management in the region. Aramburu-Merlos et al. (2015) described that the soybean water limited yield gap for this region is about 40 %, showing there is room for improving farmers’ actual yields through management adjustments. When analyzing the yield variability of a single
Conclusions
We identified management and environmental variables that affect soybean grain yield in the Argentinean Gran Chaco. We also explored key management variables that interacted with genotype MG in the same region. Crop management optimization and the closure of currently existing yield gaps are needed, and studies aiming to provide adequate management advice were not available.
Management decisions related to genotype selection, sowing date, and soil P are relevant for maximizing the crop yield.
CRediT authorship contribution statement
Andrés Madias: Conceptualization, Formal analysis, Investigation, Writing - original draft, Visualization. Guido Di Mauro: Formal analysis, Writing - review & editing. Lucas N. Vitantonio-Mazzini: Formal analysis, Writing - review & editing. Brenda L. Gambin: Formal analysis, Methodology, Writing - review & editing. Lucas Borrás: Conceptualization, Writing - review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
Authors wish to thank AAPRESID agronomists and farmers for field technical assistance and for providing their fields for experimentation, seed companies for providing seeds and financial support, and GJR Quintana (INTA – Argentinean National Institute of Agricultural Technology) for his technical coordination of trials.
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