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.

Given the wide use of the multi-climate model mean (MMM) for impact assessment studies, this work examines the fidelity of Coupled Model Intercomparison Project Phase 5 (CMIP5) in simulating the features of Indian summer monsoons as well as the post-rainy seasons for assessing the possible impacts of climate change on post-rainy season sorghum crop yields across India. The MMM simulations captured the spatial patterns and annual cycles of rainfall and surface air temperatures. However, bias was observed in the precipitation amounts and daily rainfall intensity. The trends in the simulations of MMM for both precipitation and temperatures were less satisfactory than the observed climate means. The Crop Environment Resource Synthesis (CERES)-sorghum model was used to estimate the potential impacts of future climate change on post-rainy season sorghum yield values. On average, post-rainy season sorghum yields are projected to vary between −4% and +40% as well as +10% and +59% in the near future (2040–2069) for RCP 4.5 and RCP 8.5, respectively, and between +20% and +70% (RCP 4.5) as well as +38% and +89% (RCP 8.5) in the far future (2070–2099). Even though surface air temperatures are increasing in future climate change projections, the findings suggest that an increase in the post-rainy season sorghum yields was due to an increase in the rainfall amounts up to 23% and an increase in the atmospheric CO₂ levels by the end of the 21st century. The results suggest that the projected climate change during the post-rainy season over India is an opportunity for smallholders to capitalize on the increase in rainfall amounts and further increase sorghum yields with appropriate crop management strategies.