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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]

Efforts toward the development of common bean varieties that can perform better under the drought conditions in Eastern Africa are constrained by significant genotype–environment–management (G × E × M) interactions. To address this, an attempt was made previously to assess drought stress for Eastern Africa using the Target Population of Environments (TPE) approach, albeit by using a single cultivar. Here, we extend earlier TPE work with a larger sample of representative genotypes from East Africa including commercial and breeding lines. By using a larger genotype pool, we are able to (1) assess the extent to which TPEs identified vary across genotypes (i.e., generality of the TPEs) and (2) identify best‐performing genotypes for the various environments identified. To perform the analyses, we used the CROPGRO‐DRYBEAN cropping system model available in the Decision Support System for Agrotechnology Transfer (DSSAT) v4.7 calibrated and evaluated from field trials in Africa and Colombia with 10 genotypes. Our findings indicate that despite some level of genotype‐related variation, the dominant drought stress patterns, and their priorities—drought stress‐free conditions remain the dominant TPE, followed by terminal drought stress—remain unaltered. Moreover, we find that it is possible to reduce the extent and intensity of drought stress in various TPEs by adequately targeting genotypes from the pool considered herein. Importantly, the genotype characteristics for stress‐free, extreme terminal drought and moderate terminal drought stress are significantly different, confirming a differentiated breeding strategy is required between the TPEs. The findings from this study are useful for the modernization of the bean breeding programs in Africa. [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.