Farming plant cooperation in crops - PubMed (original) (raw)
Farming plant cooperation in crops
Germain Montazeaud et al. Proc Biol Sci. 2020.
Abstract
Selection of the fittest can promote individual competitiveness but often results in the erosion of group performance. Recently, several authors revisited this idea in crop production and proposed new practices based on selection for cooperative phenotypes, i.e. phenotypes that increase crop yield through decreased competitiveness. These recommendations, however, remain difficult to evaluate without a formal description of crop evolutionary dynamics under different selection strategies. Here, we develop a theoretical framework to investigate the evolution of cooperation-related traits in crops, using plant height as a case study. Our model is tailored to realistic agricultural practices and shows that combining high plant density, high relatedness and selection among groups favours the evolution of shorter plants that maximize grain yield. Our model allows us to revisit past and current breeding practices in light of kin selection theory, and yields practical recommendations to increase cooperation among crops and promote sustainable agriculture.
Keywords: cooperation; crops; domestication; kin selection; plant breeding.
Conflict of interest statement
We declare we have no competing interests.
Figures
Figure 1.
Geometrical representation of plant competition for light. (a) We used triangles to represent drop shadows around the plants (grey triangles). The focal individual is represented in black, neighbours are represented in green. Neighbours are positioned at equal distance, d, from the focal individual. For simplicity, we here represented the left and right neighbours with the same plant height, y. Competition for light happens when triangles overlap. Competition intensity increases with increasing overlap (from yellow to red), with no competition (competition intensity 0), low competition (competition intensity 1), intermediate competition (competition intensity 2) and intense competition (competition intensity 3). (b) Relationship between focal plant fecundity, f, and focal plant height, x. We assumed that, for an isolated plant, there is an optimal plant height x^ which maximizes individual fecundity. The relationship is shown for three optimal plant height values x^=80 cm (dotted line), x^=90 cm (dashed line), and x^=100 cm (solid line). (c) Relationship between focal plant fecundity, f, and neighbours' plant height, y. We computed the cost of competition as the proportion of shaded area of the focal triangle. The relationship is shown for a focal plant height equal to the optimal plant height so that x=x^=100 cm, an angle a = π/50, and three plant–plant distances d = 10 cm (dotted line), d = 5 cm (dashed line), and d = 1 cm (solid line). Competition intensity increases from yellow to red. Symbol: ‘Wheat’ by efi kaperoni from the Noun Project. (Online version in colour.)
Figure 2.
Strategies of selection. The crop is grown in a farm composed of _n_F fields. Here, _n_F = 3, with fields respectively coloured in blue, yellow and green. In the Within-Field (WF) selection strategy, fields are sown with seeds exclusively sampled in their own harvest. Consequently, the crop evolves separately in the different fields. In the Among-Field (AF) selection strategy, seeds from the different fields are pooled together at harvest. Then, each field is sown with seeds sampled in that pool. In the Top-Field (TF) selection strategy, only the most productive field is retained for the next generation. Then, each field is sown with seeds exclusively sampled in the harvest of that top field. For all strategies, a multiplication stage precedes sowing when there are not enough founding seeds to re-populate the _n_F fields. Symbols: ‘Wheat’ by lastspark from the Noun Project, ‘seed’ by corpus delicti from the Noun Project, and ‘sow’ by Symbolon from the Noun Project. (Online version in colour.)
Figure 3.
Relationship between the mean plant height in the meta-population at equilibrium, z, and the physical distance between plants, d. Evolutionary stable strategies (ESS) predicted from the analytical model are reported as a red solid line. Results from individual-based simulation are reported in black, with dots (error bars) representing the mean (standard deviation) plant height in the meta-population after 5000 generations. Grain yield is represented in the background of the graphs and increases with lighter grey shade (see electronic supplementary material, figure S2). We tested the three seed selection strategies (WF, AF and TF) with three numbers of founding seeds _n_S = 1 (a_–_c), _n_S = 2 (d_–_f) and _n_S = 100 (g_–_i). Simulations were performed with _n_F = 20 fields and nP = 200 plants per field. In the first generation, the population was monomorphic, i.e. all plants had the same height z, with z=x^=100 cm. Phenotypic variability was then generated by mutations. Mutation rate equalled 10% meaning that on average one individual out of 10 differed in height from its parent. Mutation effect was set to ±5 cm. We varied the plant–plant physical distance from 1 to 15 cm with steps of 2 cm. After 5000 generations, the selection–mutation–drift equilibrium was reached in all simulations. Orange solid lines represent unstable equilibriums, i.e. equilibriums at which any mutation makes the mean phenotype deviate further from its original value, either in the upward or in the downward direction. (Online version in colour.)
Figure 4.
Selection efficiency. We modelled the evolution of plant height (a) and we measured the yield gains (b) realized after 20 generations with the three selection strategies (WF in red, AF in green and TF in blue). We performed simulations with _n_F = 5 fields, _n_P = 200 plants per field, and a 5 cm plant–plant distance. At generation 1 (G1), each field was initiated by sampling two founding seeds in a normally distributed plant population with mean and standard deviation on plant height, respectively, equal to 100 and 20 cm. For all subsequent generations, we draw two founding seeds in the harvest to re-populate each field. Funding seeds were multiplied in equal proportion (x100 each) to obtain 200 seeds per field. Mutation rate was equal to 10% with a ±5 cm mutation effect. Reported values are averaged over 20 simulation runs. (Online version in colour.)
References
- Darwin C. 1859. On the origin of species by means of natural selection. London, UK: John Murray.
- Donald CM, Hamblin J. 1983. The convergent evolution of annual seed crops in agriculture. In Advances in agronomy (ed. Brady NC.), pp. 97–143. New York, NY: Academic Press.
- Donald CM. 1968. The breeding of crop ideotypes. Euphytica 17, 385–403. ( 10.1007/BF00056241) -DOI
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