No abstract

Achieving global goals for sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require instantaneous access to information on food-source quality at key points of agri-food systems. Although laboratory analysis and benchtop NIR spectrometers are regularly used to quantify grain quality, these do not suit all end users, for example, stakeholders in decentralized agri-food chains that are typical in emerging economies. Therefore, we explored benchtop and portable NIR instruments, and the methods that might aid these particular end uses. For this purpose, we generated NIR spectra for 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, and sorghum) with a standard benchtop NIR spectrometer (DS2500, FOSS) and a novel portable NIR-based instrument (HL-EVT5, Hone). We explored classical deterministic methods (via winISI, FOSS), novel machine learning (ML)-driven methods (via Hone Create, Hone), and a convolutional neural network (CNN)-based method for building the calibrations to predict grain protein out of the NIR spectra. All of the tested methods enabled us to build relevant calibrations out of both types of spectra (i.e., R2 ≥ 0.90, RMSE ≤ 0.91, RPD ≥ 3.08). Generally, the calibration methods integrating the ML techniques tended to enhance the prediction capacity of the model. We also documented that the prediction of grain protein content based on the NIR spectra generated using the novel portable instrument (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the presented findings lay the foundations for the expanded use of NIR spectroscopy in agricultural research, development, and trade.

Limiting transpiration rate under high vapor pressure deficit (VPD) and/or progressive soil drying conditions are soil water conservation mechanisms that can play an important drought-adaptive role if water is limiting to support crops at its full potential. In this study, these two important physiological mechanisms were measured on parental pairs of existing Recombinant Inbred Lines (RILs) of sorghum mapping populations; both in experiments run in the glasshouse and growth chambers, and outdoors. In controlled environmental conditions, the RIL1, RIL2, RIL6 and RIL8 showed contrasting transpiration response to increasing VPD. The difference in the soil moisture fractions of transpirable soil water threshold where transpiration initiated a decline were high in RIL1, RIL3 and RIL8 respectively. The exploration of the variation of the evapotranspiration response to VPD was also carried out in a high throughput phenotyping facility in which plants were grown similar to field density conditions. Under high VPD conditions, the RIL parental pairs showed usual transpiration peak during the midday period. At this time period, genotypic differences within parental pairs were observed in RIL1, RIL2, RIL6 and RIL8. The donor parent had lower transpiration than the recurrent parents during the midday/high VPD period. Also, we found variation among parental pairs in leaf area normalized with received radiation and measured plant architecture traits. Across studied genotypes, RIL1, RIL2 and RIL8 showed differences in the plant canopy architecture and the transpiration response to an increasing VPD. Collectively, these results open the opportunity to phenotype the RIL progenies of contrasting parents and genetically map the traits controlling plant water use. In turn, this can act as an important genetic resource for identification and incorporation of terminal drought tolerance components in marker-assisted breeding.