Abstract
Mixed cropping has been suggested as a resource-efficient approach to meet high produce demands while maintaining biodiversity and minimizing environmental impact. Current breeding programs do not select for enhanced general mixing ability (GMA) and neglect biological interactions within species mixtures. Clear concepts and efficient experimental designs, adapted to breeding for mixed cropping and encoded into appropriate statistical models, are lacking. Thus, a model framework for GMA and SMA (specific mixing ability) was established. Results of a simulation study showed that an incomplete factorial design combines advantages of two commonly used full factorials, and enables to estimate GMA, SMA, and their variances in a resource-efficient way. This model was extended to the Producer (Pr) and Associate (As) concept to exploit additional information based on fraction yields. It was shown that the Pr/As concept allows to characterize genotypes for their contribution to total mixture yield, and, when relating to plant traits, allows to describe biological interaction functions (BIF) in a mixed crop. Incomplete factorial designs show the potential to drastically improve genetic gain by testing an increased number of genotypes using the same amount of resources. The Pr/As concept can further be employed to maximize GMA in an informed and efficient way. The BIF of a trait can be used to optimize species ratios at harvest as well as to extend our understanding of competitive and facilitative interactions in a mixed plant community. This study provides an integrative methodological framework to promote breeding for mixed cropping.
Highlights
Climate change, such as rising global temperatures and climatic volatility are predicted to jeopardize future agricultural productivity (Rahmstorf and Coumou, 2011)
Four experimental designs were compared for their ability to estimate general mixing ability (GMA) and specific mixing ability (SMA) variances as well as estimating genotypic effects (BLUPs) correctly in two different simulations, a “SMA-present” and a “SMA-absent” simulation
Pea GMA variance of designs C and D of the SMA-absent simulation were precisely estimated compared with design A, whereas design B estimated this parameter with lower precision (CI of ±0.11)
Summary
Climate change, such as rising global temperatures and climatic volatility are predicted to jeopardize future agricultural productivity (Rahmstorf and Coumou, 2011). The current strategies to produce stable and high yields, e.g., by the application of mineral fertilizer, are of limited future use since they themselves are a contributor to these changing climatic parameters (Thompson et al, 2019). Alternative approaches to achieve high and stable yields while maintaining biodiversity and minimizing environmental impact have to be developed. Breeding for Mixed Cropping cultivation of two or more crops on the same field. Legume/non-legume species mixtures have been proposed to achieve a higher per area production and profitability and higher yield stability with less or no external inputs (Bedoussac et al, 2015; Raseduzzaman and Jensen, 2017; Wendling et al, 2017; Viguier et al, 2018). Good pairs of complementary species have already been identified, such as combinations of corn (Zea mays L.) with cowpea (Vigna unguiculata L., Ofori and Stern, 1986), with common bean (Phaseolus vulgaris L., Hoppe, 2016; Starke, 2018), and with faba bean (Vicia faba L., Li et al, 2020), as well as small grain cereals such as barley (Hordeum vulgare L., Hauggaard-Nielsen et al, 2001) with pea (Pisum sativum L.) or wheat (Triticum aestivum L.) with faba bean (Agegnehu et al, 2006) or with lentil (Lens culinaris MEDIK., Viguier et al, 2018)
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