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Photosynthesis drives plant physiology, biomass accumulation, and yield. Photosynthetic efficiency, specifically the operating efficiency of PSII (F q'/ F m'), is highly responsive to actual growth conditions, especially to fluctuating photosynthetic photon fluence rate (PPFR). Under field conditions, plants constantly balance energy uptake to optimize growth. The dynamic regulation complicates the quantification of cumulative photochemical energy uptake based on the intercepted solar energy, its transduction into biomass, and the identification of efficient breeding lines. Here, we show significant effects on biomass related to genetic variation in photosynthetic efficiency of 178 climbing bean (Phaseolus vulgaris L.) lines. Under fluctuating conditions, the F q'/ F m' was monitored throughout the growing period using hand-held and automated chlorophyll fluorescence phenotyping. The seasonal response of F q'/ F m' to PPFR (ResponseG:PPFR) achieved significant correlations with biomass and yield, ranging from 0.33 to 0.35 and from 0.22 to 0.31 in two glasshouse and three field trials, respectively. Phenomic yield prediction outperformed genomic predictions for new environments in four trials under different growing conditions. Investigating genetic control over photosynthesis, one single nucleotide polymorphism (Chr09_37766289_13052) on chromosome 9 was significantly associated with ResponseG:PPFR in proximity to a candidate gene controlling chloroplast thylakoid formation. In conclusion, photosynthetic screening facilitates and accelerates selection for high yield potential. [ABSTRACT FROM AUTHOR]

The datasets and code presented in this article are related to the research article entitled "Comprehensiveness of conservation of useful wild plants: an operational indicator for biodiversity and sustainable development targets" 1 . The indicator methodology includes five main steps, each requiring and producing data, which are fully described and available here. These data include: species taxonomy, uses, and general geographic information (dataset 1); species occurrence data (dataset 2); global administrative areas data (dataset 3); eco-geographic predictors used in species distribution modeling (dataset 4); a world map raster file (dataset 5); species spatial distribution modeling outputs (dataset 6); ecoregion spatial data used in conservation analyses (dataset 7); protected area spatial data used in conservation analyses (dataset 8); and countries, sub-regions, and regions classifications data (dataset 9). These data are available at http://dx.doi.org/10.17632/2jxj4k32m2.1. In combination with the openly accessible methodology code (https://github.com/CIAT-DAPA/UsefulPlants-Indicator), these data facilitate indicator assessments and serve as a baseline against which future calculations of the indicator can be measured. The data can also contribute to other species distribution modeling, ecological research, and conservation analysis purposes.